difference of thesis and hypothesis

The Real Differences Between Thesis and Hypothesis (With table)

A thesis and a hypothesis are two very different things, but they are often confused with one another. In this blog post, we will explain the differences between these two terms, and help you understand when to use which one in a research project.

As a whole, the main difference between a thesis and a hypothesis is that a thesis is an assertion that can be proven or disproven, while a hypothesis is a statement that can be tested by scientific research. 

We probably need to expand a bit on this topic to make things clearer for you, let’s start with definitions and examples.

Definitions

As always, let’s start with the definition of each term before going further.

A thesis is a statement or theory that is put forward as a premise to be maintained or proved. A thesis statement is usually one sentence, and it states your position on the topic at hand.

A hypothesis is a statement that can be tested by scientific research. A hypothesis is usually based on observations, and it seeks to explain how these observations fit together.

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The best way to understand the slight difference between those terms, is to give you an example for each of them.

If you are writing a paper about the effects of climate change on the environment, your thesis might be “Climate change is causing irreparable damage to our planet, and we must take action to prevent further damage”.

If you observe that the leaves on a tree are turning yellow, your hypothesis might be “The tree is sick”. It’s the starting point of experimental research: what can you do then to prove if your hypothesis is right or wrong?

If your hypothesis is correct, then further research should be able to confirm it. However, if your hypothesis is incorrect, research will disprove it. Either way, a hypothesis is an important part of the scientific process.

Taking a look at the etymology of words can help you to remember which one to use is each case.

The word “thesis” comes from the Greek θέσις, meaning “something put forth”, and refers to an intellectual proposition.

The word “hypothesis” comes from the Greek words “hupo,” meaning “under”, and “thesis” that we just explained.

This reflects the fact that a hypothesis is an educated guess, based on observations.

Argumentation vs idea

Hypothesis are generally base on simple observation, while thesis imply that more work has been done on the topic.

A thesis is usually the result of extensive research and contemplation, and seeks to prove a point or theory.

A hypothesis is only a statement that need to be tested by observation or experimentation.

5 mains differences between thesis and hypothesis

Thesis and hypothesis are different in several ways, here are the 5 keys differences between those terms:

• A thesis is a statement that can be argued, while a hypothesis cannot be argued.
• A thesis is usually longer than a hypothesis.
• A thesis is more detailed than a hypothesis.
• A thesis is based on research, while a hypothesis may or may not be based on research.
• A thesis must be proven, while a hypothesis need not be proven.

So, in short, a thesis is an argument, while a hypothesis is a prediction. A thesis is more detailed and longer than a hypothesis, and it is based on research. Finally, a thesis must be proven, while a hypothesis does not need to be proven.

Is there a difference between a thesis and a claim?

Yes, there is a difference between a thesis and a claim. A thesis statement is usually one sentence that states your main argument, while a claim is a more general statement that can be supported by evidence.

Is a hypothesis a prediction?

No, a hypothesis is not a prediction. A prediction is a statement about what you think will happen in the future, whereas a hypothesis is a statement about what you think is causing a particular phenomenon.

What’s the difference between thesis and dissertation?

A thesis is usually shorter and more focused than a dissertation, and it is typically achieved in order to earn a bachelor’s degree. A dissertation is usually longer and more comprehensive, and it is typically completed in order to earn a master’s or doctorate degree.

What is a good thesis statement?

A good thesis statement is specific, debatable, and supports the main point of the paper. It should be clear what the researcher position is, and what evidence they will use to support it.

Thanks for reading! I hope this post helped clear up the differences between thesis and hypothesis. Like that kind of comparison? These other articles might be interesting for you:

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• The 6 Differences Between Space And Universe
• What’s the Difference Between Cosmology and Astrology?

I am very curious and I love to learn about all types of subjects. Thanks to my experience on the web, I share my discoveries with you on this site :)

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Both the hypothesis statement and the thesis statement answer a research question. 

• A hypothesis is a statement that can be proved or disproved. It is typically used in quantitative research and predicts the relationship between variables.  
• A thesis statement is a short, direct sentence that summarizes the main point or claim of an essay or research paper. It is seen in quantitative, qualitative, and mixed methods research. A thesis statement is developed, supported, and explained in the body of the essay or research report by means of examples and evidence.

Every research study should contain a concise and well-written thesis statement. If the intent of the study is to prove/disprove something, that research report will also contain a hypothesis statement.

NOTE: In some disciplines, the hypothesis is referred to as a thesis statement! This is not accurate but within those disciplines it is understood that "a short, direct sentence that summarizes the main point" will be included.

For more information, see The Research Question and Hypothesis (PDF file from the English Language Support, Department of Student Services, Ryerson University).

How do I write a good thesis statement?

How do I write a good hypothesis statement?

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Thesis Vs Hypothesis: Understanding The Basis And The Key Differences

Hypothesis vs. thesis: They sound similar and seem to discuss the same thing. However, these terms have vastly different meanings and purposes. You may have encountered these concepts in school or research, but understanding them is key to executing quality work. 

As an inexperienced writer, the thought of differentiating between hypotheses and theses might seem like an insurmountable task. Fortunately, I am here to help. 

In this article, I’ll discuss hypothesis vs. thesis, break down their differences, and show you how to apply this knowledge to create quality written works. Let’s get to it!

Thesis vs. Hypothesis: Understanding the Basis

The power of a thesis.

A thesis is a foundational element in academic writing and research. It also serves as the linchpin of your argument, encapsulating the central idea or point you aim to prove or disprove throughout your work. 

A thesis statement is typically found at the end of the introduction in an essay or research paper, succinctly summarizing the overarching theme.

Crafting a strong thesis

• Understand the research: Begin by thoroughly comprehending the requirements and objectives of your research. Having a clear understanding of the topic you are arguing or analyzing is crucial.
• Choose a clear topic: Choose one that interests you and aligns with the research’s scope. Clarity and focus are essential in crafting a strong thesis.
• Conduct research: Gather relevant information and sources to develop a deep understanding of your topic. This research will provide the evidence and context for your thesis.
• Identify your position: Determine your stance or position on the topic. Your thesis should express a clear opinion or argument you intend to support throughout your work.
• Narrow down your focus: Refine your topic and thesis more precisely. Avoid broad, generalized statements. Instead, aim for a concise and specific thesis that addresses a particular aspect of the topic.
• Test for validity: Ensuring that you can argue and provide evidence to support your thesis is crucial. It should not be a self-evident or universally accepted fact.
• Write and revise: Craft your thesis statement as a clear, concise sentence summarizing your main argument. Revise and refine it as needed to improve its clarity and strength.

Remember that a strong thesis serves as the foundation for your entire piece of writing, guiding your readers and keeping your work focused and organized.

Hypothesis: The scientific proposition

In contrast, a hypothesis is a tentative proposition or educated guess. It is the initial step in the scientific method, where researchers formulate a hunch to test their assumptions and theories. 

A hypothesis is an assertion that can be proven or disproven through experimentation and observation.

Formulating a hypothesis

• Identify the research question: Identify the research question or problem you want to investigate. Clearly define the scope and boundaries of your inquiry.
• Review existing knowledge: Conduct a literature review to gather information about the topic. Understand the existing body of knowledge and literature in the field.
• Formulate a tentative explanation: Based on your research and understanding of the topic, create a tentative explanation or educated guess about the phenomenon you are studying. This should be a statement that can be falsifiable through experimentation or observation.
• Make it testable: Ensure that your hypothesis is testable and falsifiable. In other words, designing experiments or gathering data supporting or refuting your hypothesis should be possible.
• Specify variables and predictions: Clearly define the variables involved in your hypothesis and make predictions about how changes in these variables will affect the outcome. It also helps in designing experiments and collecting data to test your hypothesis.

Formulating a hypothesis is a crucial step in the scientific method since it directs research and guides efforts to validate theories or uncover new knowledge.

Key Differences Between Thesis vs. Hypothesis

1. Nature of statement

• Thesis: A thesis presents a clear and definitive statement or argument that summarizes the main point of a research paper or essay.
• Hypothesis: A hypothesis is a tentative and testable proposition or educated guess that suggests a possible outcome of an experiment or research study.
• Thesis: The primary purpose of a thesis is to provide a central focus and roadmap for the entire piece of academic writing.
• Hypothesis: The main purpose of a hypothesis is to guide scientific research by proposing a specific prediction that can be tested and validated.

3. Testability

• Thesis: A thesis is not typically subjected to experimentation but serves as a point of argumentation and discussion.
• Hypothesis: A hypothesis, on the other hand, is explicitly designed for testing through experimentation or observation, making it a fundamental part of the scientific method.

4. Research stage

• Thesis: A thesis is usually formulated after extensive research and analysis as a conclusion or summary of findings.
• Hypothesis: A hypothesis is formulated at the beginning of a research project to establish a basis for experimentation and data collection.
• Thesis: A thesis typically encompasses the entire research paper or essay, providing an overarching theme throughout the work.
• Hypothesis: A hypothesis addresses a specific aspect of a research question or problem, guiding the focus of experiments or investigations.

6. Examples

• Thesis: Example of a thesis statement: “The impact of climate change on marine ecosystems is irreversible.”
• Hypothesis: Example of a hypothesis: “If increased temperatures continue, coral reefs will experience bleaching events.”
• Thesis: The thesis represents a conclusion or a well-supported argument and does not aim to be proven or disproven.
• Hypothesis: On the other hand, a hypothesis aims to be tested and validated through empirical evidence. Besides, it can be proven true or false based on the results of experiments or observations.

These differences highlight the distinct roles that the thesis and hypothesis play in academic writing and scientific research, with one providing a point of argumentation and the other guiding the scientific inquiry process.

Can a hypothesis become a thesis?

Yes. A hypothesis can develop into a thesis as it accumulates substantial evidence through research.

Do all research papers require a thesis?

Not necessarily. While most academic papers benefit from a clear thesis, some, like purely descriptive papers, may follow a different structure.

Can a thesis be proven wrong?

Yes. The purpose of a thesis is not only to prove but also to encourage critical analysis. It can be proven wrong with compelling counterarguments and evidence.

How long should a thesis statement be?

A thesis statement should be concise and to the point, typically one or two sentences.

Is a hypothesis only used in scientific research?

Although hypotheses are typically linked to scientific research, they can also be used to verify assumptions and theories in other areas.

Can a hypothesis be vague?

No. When creating a hypothesis, it’s important to make it clear and able to be tested. Developing experiments and making conclusions based on the results can be difficult if the hypothesis needs clarification.

Final Thoughts

In conclusion, understanding the differences between a hypothesis and a thesis is vital to crafting successful research projects and academic papers. While they may seem interchangeable at first glance, these two concepts serve distinct purposes in the research process. 

A hypothesis serves as a testable prediction or explanation, whereas a thesis is the central argument of a paper or project. Your work can lack clarity and purpose without understanding the difference. 

So, the next time you embark on a research project, take the time to ensure that you understand the fundamental difference between a hypothesis and a thesis. Doing so can lead to more focused, meaningful research that advances knowledge and understanding in your field.

You can also learn more about how long a thesis statement should be .

Thanks for reading.

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Home » Education » Difference Between Thesis and Hypothesis

Difference Between Thesis and Hypothesis

Main difference –  thesis vs hypothesis                           .

Thesis and hypothesis are two common terms that are often found in research studies. Hypothesis is a logical proposition that is based on existing knowledge that serves as the starting point of an investigation. A thesis is a statement that is put forward as a premise to be maintained or proved. The main difference between thesis and hypothesis is that thesis is found in all research studies whereas a hypothesis is mainly found in experimental quantitative research studies.

This article explains,

1. What is a Thesis?      – Definition, Features, Function

2. What is a Hypothesis?      – Definition, Features, Function

What is a Thesis

The word thesis has two meanings in a research study. Thesis can either refer to a dissertation or a thesis statement. Thesis or dissertation is the long essay or document that consists of the research study.  Thesis can also refer to a theory or statement that is used as a premise to be maintained or proved.

The thesis statement in a research article is a sentence found at the beginning of the paper that presents the main argument of the paper. The rest of the document will gather, organize and present evidence to support this argument. The thesis statement will basically present the topic of the paper and indicate what position the researcher is going to take in relation to this topic. A thesis statement can generally be found at the end of the first paragraph (introductory paragraph) of the paper.

What is a Hypothesis

A hypothesis is a logical assumption based on available evidence. Hypothesis is defined as “a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation” in the Oxford dictionary and as “an idea or theory that is not proven but that leads to further study or discussion” in the Merriam-Webster dictionary. In simple words, it is an educated guess that is not proven with concrete scientific evidence. Once it is scientifically tested and proven, it becomes a theory. However, it is important to note that a hypothesis can be accurate or inaccurate.

Hypotheses are mostly used in experiments and research studies. However, hypotheses are not used in every research study. They are mostly used in quantitative research studies  that deal with experiments. Hypotheses are often used to test a specific model or theory . They can be used only when the researcher has sufficient knowledge about the subject since hypothesis are always based on the existing knowledge. Once the hypothesis is built, the researcher can find and analyze data and use them to prove or disprove the hypothesis.

Thesis: A thesis is a “statement or theory that is put forward as a premise to be maintained or proved” or a “long essay or dissertation involving personal research, written by a candidate for a university degree” (Oxford dictionary).

Hypothesis: A hypothesis is “a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation” (Oxford dictionary).

Thesis: Thesis statement can be found in all research papers.

Hypothesis: Hypotheses are usually found in experimental quantitative research studies.

Thesis: Thesis statement may explain the hypothesis and how the researcher intends to support it.

Hypothesis: Hypothesis is an educated guess based on the existing knowledge.

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• What Is a Thesis? | Ultimate Guide & Examples

What Is a Thesis? | Ultimate Guide & Examples

Published on September 14, 2022 by Tegan George . Revised on November 21, 2023.

A thesis is a type of research paper based on your original research. It is usually submitted as the final step of a master’s program or a capstone to a bachelor’s degree.

Writing a thesis can be a daunting experience. Other than a dissertation , it is one of the longest pieces of writing students typically complete. It relies on your ability to conduct research from start to finish: choosing a relevant topic , crafting a proposal , designing your research , collecting data , developing a robust analysis, drawing strong conclusions , and writing concisely .

Thesis template

You can also download our full thesis template in the format of your choice below. Our template includes a ready-made table of contents , as well as guidance for what each chapter should include. It’s easy to make it your own, and can help you get started.

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Table of contents

Thesis vs. thesis statement, how to structure a thesis, acknowledgements or preface, list of figures and tables, list of abbreviations, introduction, literature review, methodology, reference list, proofreading and editing, defending your thesis, other interesting articles, frequently asked questions about theses.

You may have heard the word thesis as a standalone term or as a component of academic writing called a thesis statement . Keep in mind that these are two very different things.

• A thesis statement is a very common component of an essay, particularly in the humanities. It usually comprises 1 or 2 sentences in the introduction of your essay , and should clearly and concisely summarize the central points of your academic essay .
• A thesis is a long-form piece of academic writing, often taking more than a full semester to complete. It is generally a degree requirement for Master’s programs, and is also sometimes required to complete a bachelor’s degree in liberal arts colleges.
• In the US, a dissertation is generally written as a final step toward obtaining a PhD.
• In other countries (particularly the UK), a dissertation is generally written at the bachelor’s or master’s level.

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The final structure of your thesis depends on a variety of components, such as:

• Your discipline
• Your theoretical approach

Humanities theses are often structured more like a longer-form essay . Just like in an essay, you build an argument to support a central thesis.

In both hard and social sciences, theses typically include an introduction , literature review , methodology section ,  results section , discussion section , and conclusion section . These are each presented in their own dedicated section or chapter. In some cases, you might want to add an appendix .

Thesis examples

We’ve compiled a short list of thesis examples to help you get started.

• Example thesis #1:   “Abolition, Africans, and Abstraction: the Influence of the ‘Noble Savage’ on British and French Antislavery Thought, 1787-1807” by Suchait Kahlon.
• Example thesis #2: “’A Starving Man Helping Another Starving Man’: UNRRA, India, and the Genesis of Global Relief, 1943-1947″ by Julian Saint Reiman.

The very first page of your thesis contains all necessary identifying information, including:

• Your full title
• Your full name
• Your department
• Your institution and degree program
• Your submission date.

Sometimes the title page also includes your student ID, the name of your supervisor, or the university’s logo. Check out your university’s guidelines if you’re not sure.

Read more about title pages

The acknowledgements section is usually optional. Its main point is to allow you to thank everyone who helped you in your thesis journey, such as supervisors, friends, or family. You can also choose to write a preface , but it’s typically one or the other, not both.

Read more about acknowledgements Read more about prefaces

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An abstract is a short summary of your thesis. Usually a maximum of 300 words long, it’s should include brief descriptions of your research objectives , methods, results, and conclusions. Though it may seem short, it introduces your work to your audience, serving as a first impression of your thesis.

Read more about abstracts

A table of contents lists all of your sections, plus their corresponding page numbers and subheadings if you have them. This helps your reader seamlessly navigate your document.

Your table of contents should include all the major parts of your thesis. In particular, don’t forget the the appendices. If you used heading styles, it’s easy to generate an automatic table Microsoft Word.

Read more about tables of contents

While not mandatory, if you used a lot of tables and/or figures, it’s nice to include a list of them to help guide your reader. It’s also easy to generate one of these in Word: just use the “Insert Caption” feature.

Read more about lists of figures and tables

If you have used a lot of industry- or field-specific abbreviations in your thesis, you should include them in an alphabetized list of abbreviations . This way, your readers can easily look up any meanings they aren’t familiar with.

Read more about lists of abbreviations

Relatedly, if you find yourself using a lot of very specialized or field-specific terms that may not be familiar to your reader, consider including a glossary . Alphabetize the terms you want to include with a brief definition.

Read more about glossaries

An introduction sets up the topic, purpose, and relevance of your thesis, as well as expectations for your reader. This should:

• Ground your research topic , sharing any background information your reader may need
• Define the scope of your work
• Introduce any existing research on your topic, situating your work within a broader problem or debate
• State your research question(s)
• Outline (briefly) how the remainder of your work will proceed

In other words, your introduction should clearly and concisely show your reader the “what, why, and how” of your research.

Read more about introductions

A literature review helps you gain a robust understanding of any extant academic work on your topic, encompassing:

• Selecting relevant sources
• Determining the credibility of your sources
• Critically evaluating each of your sources
• Drawing connections between sources, including any themes, patterns, conflicts, or gaps

A literature review is not merely a summary of existing work. Rather, your literature review should ultimately lead to a clear justification for your own research, perhaps via:

• Addressing a gap in the literature
• Building on existing knowledge to draw new conclusions
• Exploring a new theoretical or methodological approach
• Introducing a new solution to an unresolved problem
• Definitively advocating for one side of a theoretical debate

Read more about literature reviews

Theoretical framework

Your literature review can often form the basis for your theoretical framework, but these are not the same thing. A theoretical framework defines and analyzes the concepts and theories that your research hinges on.

Read more about theoretical frameworks

Your methodology chapter shows your reader how you conducted your research. It should be written clearly and methodically, easily allowing your reader to critically assess the credibility of your argument. Furthermore, your methods section should convince your reader that your method was the best way to answer your research question.

A methodology section should generally include:

• Your overall approach ( quantitative vs. qualitative )
• Your research methods (e.g., a longitudinal study )
• Your data collection methods (e.g., interviews or a controlled experiment
• Any tools or materials you used (e.g., computer software)
• The data analysis methods you chose (e.g., statistical analysis , discourse analysis )
• A strong, but not defensive justification of your methods

Read more about methodology sections

Your results section should highlight what your methodology discovered. These two sections work in tandem, but shouldn’t repeat each other. While your results section can include hypotheses or themes, don’t include any speculation or new arguments here.

Your results section should:

• State each (relevant) result with any (relevant) descriptive statistics (e.g., mean , standard deviation ) and inferential statistics (e.g., test statistics , p values )
• Explain how each result relates to the research question
• Determine whether the hypothesis was supported

Additional data (like raw numbers or interview transcripts ) can be included as an appendix . You can include tables and figures, but only if they help the reader better understand your results.

Read more about results sections

Your discussion section is where you can interpret your results in detail. Did they meet your expectations? How well do they fit within the framework that you built? You can refer back to any relevant source material to situate your results within your field, but leave most of that analysis in your literature review.

For any unexpected results, offer explanations or alternative interpretations of your data.

Read more about discussion sections

Your thesis conclusion should concisely answer your main research question. It should leave your reader with an ultra-clear understanding of your central argument, and emphasize what your research specifically has contributed to your field.

Why does your research matter? What recommendations for future research do you have? Lastly, wrap up your work with any concluding remarks.

Read more about conclusions

In order to avoid plagiarism , don’t forget to include a full reference list at the end of your thesis, citing the sources that you used. Choose one citation style and follow it consistently throughout your thesis, taking note of the formatting requirements of each style.

Which style you choose is often set by your department or your field, but common styles include MLA , Chicago , and APA.

Create APA citations Create MLA citations

In order to stay clear and concise, your thesis should include the most essential information needed to answer your research question. However, chances are you have many contributing documents, like interview transcripts or survey questions . These can be added as appendices , to save space in the main body.

Read more about appendices

Once you’re done writing, the next part of your editing process begins. Leave plenty of time for proofreading and editing prior to submission. Nothing looks worse than grammar mistakes or sloppy spelling errors!

Consider using a professional thesis editing service or grammar checker to make sure your final project is perfect.

Once you’ve submitted your final product, it’s common practice to have a thesis defense, an oral component of your finished work. This is scheduled by your advisor or committee, and usually entails a presentation and Q&A session.

After your defense , your committee will meet to determine if you deserve any departmental honors or accolades. However, keep in mind that defenses are usually just a formality. If there are any serious issues with your work, these should be resolved with your advisor way before a defense.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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The conclusion of your thesis or dissertation shouldn’t take up more than 5–7% of your overall word count.

If you only used a few abbreviations in your thesis or dissertation , you don’t necessarily need to include a list of abbreviations .

If your abbreviations are numerous, or if you think they won’t be known to your audience, it’s never a bad idea to add one. They can also improve readability, minimizing confusion about abbreviations unfamiliar to your reader.

When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

Generally, an outline contains information on the different sections included in your thesis or dissertation , such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review , research methods , avenues for future research, etc.)

A thesis is typically written by students finishing up a bachelor’s or Master’s degree. Some educational institutions, particularly in the liberal arts, have mandatory theses, but they are often not mandatory to graduate from bachelor’s degrees. It is more common for a thesis to be a graduation requirement from a Master’s degree.

Even if not mandatory, you may want to consider writing a thesis if you:

• Plan to attend graduate school soon
• Have a particular topic you’d like to study more in-depth
• Are considering a career in research
• Would like a capstone experience to tie up your academic experience

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B oth the hypothesis statement and the thesis statement answer the research question of the study.  When the statement is one that can be proved or disproved, it is an hypothesis statement.  If, instead, the statement specifically shows the intentions/objectives/position of the researcher, it is a thesis statement.

A hypothesis is a statement that can be proved or disproved.  It is typically used in quantitative research and predicts the relationship between variables.

A thesis statement is a short, direct sentence that summarizes the main point or claim of an essay or research paper. It is seen in quantitative, qualitative, and mixed methods research.  A thesis statement is developed, supported, and explained in the body of the essay or research report by means of examples and evidence.

Every research study should contain a concise and well-written thesis statement. If the intent of the study is to prove/disprove something, that research report will also contain an hypothesis statement.

Jablonski , Judith. What is the difference between a thesis statement and an hypothesis statement? Online Library. American Public University System. Jun 16, 2014. Web.   http://apus.libanswers.com/faq/2374

Let’s say you are interested in the conflict in Darfur, and you conclude that the issues you wish to address include the nature, causes, and effects of the conflict, and the international response. While you could address the issue of international response first, it makes the most sense to start with a description of the conflict, followed by an exploration of the causes, effects, and then to discuss the international response and what more could/should be done.

This hypothetical example may lead to the following title, introduction, and statement of questions:

Conflict in Darfur: Causes, Consequences, and International Response       This paper examines the conflict in Darfur, Sudan. It is organized around the following questions: (1) What is the nature of the conflict in Darfur? (2) What are the causes and effects of the conflict? (3) What has the international community done to address it, and what more could/should it do?

Following the section that presents your questions and background, you will offer a set of responses/answers/(hypo)theses. They should follow the order of the questions. This might look something like this, “The paper argues/contends/ maintains/seeks to develop the position that...etc.” The most important thing you can do in this section is to present as clearly as possible your best thinking on the subject matter guided by course material and research. As you proceed through the research process, your thinking about the issues/questions will become more nuanced, complex, and refined. The statement of your theses will reflect this as you move forward in the research process.

So, looking to our hypothetical example on Darfur:

The current conflict in Darfur goes back more than a decade and consists of fighting between government-supported troops and residents of Darfur. The causes of the conflict include x, y, and z. The effects of the conflict have been a, b, and c. The international community has done 0, and it should do 1, 2, and 3.

Once you have setup your thesis you will be ready to begin amassing supporting evidence for you claims. This is a very important part of the research paper, as you will provide the substance to defend your thesis.

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Thesis vs. Hypothesis: What's the Difference?

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Thesis vs Hypothesis vs Theory: the Differences and examples

Many students may have a hard time understanding the differences between a thesis, a hypothesis, and a theory. It is important to understand their differences. Such an understanding will be instrumental.

More so, when writing complex research papers that require a thesis that has a hypothesis and utilizes theories. We have gathered from responses of our college writing service that the difference between the three is confusing.

That being said, this article is meant to explain the differences between a thesis, a hypothesis, and a theory. 

Difference between Hypothesis and Thesis

There are major differences between hypothesis and thesis. While they seem to be related on the face, their differences are huge both in concept and practice.

A hypothesis is a proposed explanation of something or a phenomenon. A scientific hypothesis uses a scientific method that requires any hypothesis to be tested. As such, scientists and researchers base their hypothesis on observations that have been previously made and that which cannot be explained by the available or prevailing scientific theories.

From the definition of a hypothesis, you can see that theories must be included in any scientific method. This is the reason why this article tries to differentiate a thesis, a hypothesis, and a theory. 

Moving forward, a thesis can be defined as a written piece of academic work that is submitted by students to attain a university degree. However, on a smaller scale, there is something that is referred to as a thesis statement.

This is written at the introduction of a research paper or essay that is supported by a credible argument. The link between a hypothesis and thesis is that a thesis is a distinction or an affirmation of the hypothesis.

What this means is that whenever a research paper contains a hypothesis, there should be a thesis that validates it. 

What is a Hypothesis?

A hypothesis can be defined as the proposed or suggested explanation for an occurrence, something, or a phenomenon. It should be testable through scientific methods. The reason why scholarly works should have a hypothesis is that the observed phenomena could not be explained using the prevailing scientific theories hence the reason why it should be tested. 

Testing the hypothesis may result in the development of new or improved scientific theories that are beneficial to the discipline and society in general. 

What is a Thesis?

A thesis is a written piece of academic work that is submitted by students to attain a university degree. When a thesis is used as a stand-alone word, it denotes academic papers written by university students. It is mostly written by those pursuing postgraduate degrees, at the end of their courses. They demonstrate their proficiency in their disciplines and the topics they have selected for research. 

However, when a thesis is used to refer to a statement, it denotes the statement that is written at the introduction of a research paper or essay. A thesis is supported by a credible argument.

Every research paper must have a thesis statement that acts as a guide to what the research will be all about. It is possible to receive very poor grades or even score a zero if your research paper lacks the thesis statement. 

What is a Theory?

A theory can be defined as a rational form of abstract perspectives or thinking concerning the results of such thinking or a phenomenon. The process of rational and contemplative thinking is mostly associated with processes such as research or observational study.

As such, a theory can be considered to belong to both scientific and non-scientific disciplines. Theories can also belong to no discipline.

From a modernistic scientific approach, a theory can mean scientific theories that have been well confirmed to explain nature and that are created in such a way that they are consistent with the standard scientific method. A theory should fulfill all the criteria required by modern-day science. 

A theory should be described in a way that scientific tests that have been conducted can provide empirical support or contradiction to the theory.

Because of the nature by which scientific theories are developed, they tend to be the most rigorous, reliable, and comprehensive when it comes to describing and supporting scientific knowledge. 

The connection between a theory and a hypothesis is that when a theory has not yet been proven, it can be referred to as a hypothesis.

The thing about theories is that they are not meant to help the scientist or researcher reach a particular goal. Rather, a theory is meant to guide the process of finding facts about a phenomenon or an observation. 

Difference between a Theory and Thesis

A theory is a rational form of abstract perspectives or thinking concerning the results of such thinking or a phenomenon. The process of rational and contemplative thinking is mostly associated with processes such as research or observational study. On the other hand, a thesis is a written piece of academic work that is submitted by students to attain a university degree.

It denotes academic papers that are written by students in the university, especially those pursuing postgraduate degrees, at the end of their courses to demonstrate their proficiency in their disciplines and the topics they have selected for research. 

To understand the application of these, read our guide on the difference between a research paper and a thesis proposal to get a wider view.

How to write a Good Hypothesis

1. asking a question.

Asking a question is the first step in the scientific method and the question should be based on  who, what, where, when, why,  and  how . The question should be focused, specific, and researchable.

2. Gathering preliminary research 

This is the process of collecting relevant data. It can be done by researching academic journals, conducting case studies, observing phenomena, and conducting experiments. 

3. Formulating an answer

When the research is completed, you should think of how best to answer the question and defend your position. The answer to your question should be objective. 

4. Writing the hypothesis

When your answer is ready, you can move to the next step of formulating the hypothesis. A good hypothesis should contain relevant variables, predicted outcomes, and a study group that can include non-human things. The hypothesis should not be a question but a complete statement. 

5. Refining the hypothesis

Though you may skip this step, it is advisable to include it because your study may involve two groups or be a correlational study. Refining the hypothesis will ensure that you have stated the difference or relationship you expect to find. 

6. Creating a null and alternative hypotheses

A null hypothesis (H0) will postulate that there is no evidence to support the difference. On the other hand, an alternative hypothesis (H1) posits that there is evidence in support of the difference. 

Difference between thesis and hypothesis example

Thesis:  High levels of alcohol consumption have detrimental effects on your health, such as weight gain, heart disease, and liver complications.

Hypothesis:  The people who consume high levels of alcohol experience detrimental effects on their health such as weight gain, heart disease, and liver complications. 

What is the difference between a summary and a thesis statement?

A summary is a brief account or statement of the main points from the researches. A thesis statement is a statement that is written at the end of the introduction of a research paper or essay that summarizes the main claims of the paper. 

Difference between hypothesis and statement of the problem

A hypothesis can be defined as the proposed or suggested explanation for an occurrence, something, or a phenomenon. The same should be testable through scientific methods. Conversely, a statement of a problem is a concise description of the issue to be addressed on how it can be improved. 

When not handling complex essays and academic writing tasks, Josh is busy advising students on how to pass assignments. In spare time, he loves playing football or walking with his dog around the park.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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Thesis vs. Hypothesis — What's the Difference?

Difference Between Thesis and Hypothesis

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Dissertation vs Thesis: The Differences that Matter

As a graduate student, you will have many different types of challenging coursework and assignments. However, the biggest project that you’ll work on when earning your master’s or doctoral degree will be your thesis or dissertation . The differences between a dissertation vs thesis are plenty. That’s because each of these pieces of writing happen at different times in one’s educational journey.

Let’s break down what a dissertation and thesis are so that you have a strong handle on what’s expected. For both a thesis and a dissertation, there is an obvious fluency and understanding of the subject one studies.

Let’s take a look at their similarities and differences.

Photo by  Glenn Carstens-Peters  on  Unsplash

What is a dissertation.

When you enter a doctoral program to earn a PhD, you will learn a lot about how to conduct your own research. At the culmination of your degree program, you’ll produce a dissertation.

A dissertation is a lengthy piece of written work that includes original research or expanded research on a new or existing topic. As the doctoral student, you get to choose what you want to explore and write about within your field of study.

What is a Thesis?

A thesis is also a scholarly piece of writing, but it is for those who are graduating from a master’s program. A thesis allows students to showcase their knowledge and expertise within the subject matter they have been studying.

Main Differences Between a Thesis vs. Dissertation

The biggest difference between a thesis and a dissertation is that a thesis is based on existing research.

On the other hand, a dissertation will more than likely require the doctoral student to conduct their own research and then perform analysis. The other big difference is that a thesis is for master’s students and the dissertation is for PhD students.

Structural Differences Between a Thesis and a Dissertation

Structurally, the two pieces of written analysis have many differences.

• A thesis is at least 100 pages in length
• A dissertation is 2-3x that in length
• A thesis expands upon and analyzes existing research
• A dissertation’s content is mostly attributed to the student as the author

Research Content and Oral Presentation

Once completed, some programs require students to orally present their thesis and dissertation to a panel of faculty members.

Typically, a dissertation oral presentation can take several hours. On the other hand, a thesis only takes about an hour to present and answer questions.

Let’s look at how the two scholarly works are similar and different:

Similarities:

• Each is considered a final project and required to graduate
• Both require immense understanding of the material
• Written skills are key to complete both
• Neither can be plagiarized
• Both are used to defend an argument
• Both require analytical skills
• You will have to draft, rewrite, and edit both pieces of writing
• For both, it is useful to have another person look over before submission
• Both papers are given deadlines

Differences:

• A dissertation is longer than a thesis
• A dissertation requires new research
• A dissertation requires a hypothesis that is then proven
• A thesis chooses a stance on an existing idea and defends it with analysis
• A dissertation has a longer oral presentation component

The Differences in Context: Location Matters

The united states.

In the US, everything that was previously listed is how schools differentiate between a thesis and a dissertation. A thesis is performed by master’s students, and a dissertation is written by PhD candidates.

In Europe, the distinction between a thesis and dissertation becomes a little more cloudy. That’s because PhD programs may require a doctoral thesis to graduate. Then, as a part of a broader post-graduate research project, students may complete a dissertation.

Photo by  Russ Ward  on  Unsplash

The purpose behind written research.

Each piece of writing is an opportunity for a student to demonstrate his or her ability to think critically, express their opinions in writing, and present their findings in front of their department.

Graduate degrees take a lot of time, energy, and hard work to complete. When it comes to writing such lengthy and informative pieces, there is a lot of time management that is involved. The purpose of both a thesis and a dissertation are written proof that you understand and have mastered the subject matter of your degree.

Degree Types

A doctoral degree, or PhD, is the highest degree that one can earn. In most cases, students follow the following path to achieve this level of education: Earn a bachelor’s degree, then a master’s, and then a PhD. While not every job title requires this deep educational knowledge, the salaries that come along with each level of higher education increase accordingly.

Earning Your Degree

Whether you are currently a prospective student considering earning your higher education degree or a student enrolled in a master’s or doctoral program, you know the benefits of education.

However, for some, earning a traditional degree on-campus doesn’t make sense. This could be because of the financial challenges, familial obligations, accessibility, or any other number of reasons.

For students who are seeking their higher education degrees but need a flexible, affordable, and quality alternative to traditional college, take a look at the programs that the University of the People has to offer.

University of the People is an entirely online, US accredited and tuition-free institution dedicated to higher education. You can earn your Master’s in Business Administration or your Master’s in Education . Not to mention, there are a handful of associate’s and bachelor’s degree programs to choose from as well.

If you want to learn more, get in touch with us !

The Bottom Line

Regardless of where and when you earn your master’s or doctoral degree, you will likely have to complete a thesis or dissertation. The main difference between a thesis and dissertation is the level at which you complete them. A thesis is for a master’s degree, and a dissertation is for a doctoral degree.

Don’t be overwhelmed by the prospect of having to research and write so much. Your educational journey has prepared you with the right time management skills and writing skills to make this feat achievable!

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“Theory” vs. “Hypothesis”: What Is The Difference?

Chances are you’ve heard of the TV show The Big Bang Theory . Lots of people love this lighthearted sitcom for its quirky characters and their relationships, but others haven’t even given the series a chance for one reason: they don’t like science and assume the show is boring.

However, it only takes a few seconds with Sheldon and Penny to disprove this assumption and realize that this theory ab0ut The Big Bang Theory is wrong—it isn’t a scientific snoozefest.

But wait: is it a theory or a  hypothesis about the show that leads people astray? And would the actual big bang theory— the one that refers to the beginning of the universe—mean the same thing as a big bang hypothesis ?

Let’s take a closer look at theory and hypothesis to nail down what they mean.

What does theory mean?

As a noun, a theory is a group of tested general propositions “commonly regarded as correct, that can be used as principles of explanation and prediction for a class of phenomena .” This is what is known as a scientific   theory , which by definition is “an understanding that is based on already tested data or results .” Einstein’s theory of relativity and the  theory of evolution are both examples of such tested propositions .

Theory is also defined as a proposed explanation you might make about your own life and observations, and it’s one “whose status is still conjectural and subject to experimentation .” For example:  I’ve got my own theories about why he’s missing his deadlines all the time.  This example refers to an idea that has not yet been proven.

There are other uses of the word theory as well.

• In this example,  theory is “a body of principles or theorems belonging to one subject.” It can be a branch of science or art that deals with its principles or methods .
• For example: when she started to follow a new parenting theory based on a trendy book, it caused a conflict with her mother, who kept offering differing opinions .

First recorded in 1590–1600, theory originates from the Late Latin theōria , which stems from the Greek theōría. Synonyms for theory include approach , assumption , doctrine , ideology , method , philosophy , speculation , thesis , and understanding .

What does hypothesis mean?

Hypothesis is a noun that means “a proposition , or set of propositions, set forth as an explanation” that describe “some specified group of phenomena.” Sounds familiar to theory , no?

But, unlike a theory , a scientific  hypothesis is made before testing is done and isn’t based on results. Instead, it is the basis for further investigation . For example: her working hypothesis is that this new drug also has an unintended effect on the heart, and she is curious what the clinical trials  will show .

Hypothesis also refers to “a proposition assumed as a premise in an argument,” or “mere assumption or guess.” For example:

• She decided to drink more water for a week to test out her hypothesis that dehydration was causing her terrible headaches.
• After a night of her spouse’s maddening snoring, she came up with the hypothesis that sleeping on his back was exacerbating the problem.

Hypothesis was first recorded around 1590–1600 and originates from the Greek word hypóthesis (“basis, supposition”). Synonyms for hypothesis include: assumption , conclusion , conjecture , guess , inference , premise , theorem , and thesis .

How to use each

Although theory in terms of science is used to express something based on extensive research and experimentation, typically in everyday life, theory is used more casually to express an educated guess.

So in casual language,  theory and hypothesis are more likely to be used interchangeably to express an idea or speculation .

In most everyday uses, theory and hypothesis convey the same meaning. For example:

• Her opinion is just a theory , of course. She’s just guessing.
• Her opinion is just a hypothesis , of course. She’s just guessing.

It’s important to remember that a scientific   theory is different. It is based on tested results that support or substantiate it, whereas a hypothesis is formed before the research.

For example:

• His  hypothesis  for the class science project is that this brand of plant food is better than the rest for helping grass grow.
• After testing his hypothesis , he developed a new theory based on the experiment results: plant food B is actually more effective than plant food A in helping grass grow.

In these examples, theory “doesn’t mean a hunch or a guess,” according to Kenneth R. Miller, a cell biologist at Brown University. “A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

So if you have a concept that is based on substantiated research, it’s a theory .

But if you’re working off of an assumption that you still need to test, it’s a hypothesis .

So remember, first comes a hypothesis , then comes theory . Now who’s ready for a  Big Bang Theory marathon?

Now that you’ve theorized and hypothesized through this whole article … keep testing your judgment (Or is it judgement?). Find out the correct spelling here!

Or find out the difference between these two common issues below!

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Frequently asked questions

What is the difference between a dissertation and a thesis.

The words ‘ dissertation ’ and ‘thesis’ both refer to a large written research project undertaken to complete a degree, but they are used differently depending on the country:

• In the UK, you write a dissertation at the end of a bachelor’s or master’s degree, and you write a thesis to complete a PhD.
• In the US, it’s the other way around: you may write a thesis at the end of a bachelor’s or master’s degree, and you write a dissertation to complete a PhD.

Frequently asked questions: Knowledge Base

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).

In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarise yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a dissertation , thesis, research paper , or proposal .

The literature review usually comes near the beginning of your  dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

Harvard referencing uses an author–date system. Sources are cited by the author’s last name and the publication year in brackets. Each Harvard in-text citation corresponds to an entry in the alphabetised reference list at the end of the paper.

Vancouver referencing uses a numerical system. Sources are cited by a number in parentheses or superscript. Each number corresponds to a full reference at the end of the paper.

A Harvard in-text citation should appear in brackets every time you quote, paraphrase, or refer to information from a source.

The citation can appear immediately after the quotation or paraphrase, or at the end of the sentence. If you’re quoting, place the citation outside of the quotation marks but before any other punctuation like a comma or full stop.

In Harvard referencing, up to three author names are included in an in-text citation or reference list entry. When there are four or more authors, include only the first, followed by ‘ et al. ’

A bibliography should always contain every source you cited in your text. Sometimes a bibliography also contains other sources that you used in your research, but did not cite in the text.

MHRA doesn’t specify a rule about this, so check with your supervisor to find out exactly what should be included in your bibliography.

Footnote numbers should appear in superscript (e.g. 11 ). You can use the ‘Insert footnote’ button in Word to do this automatically; it’s in the ‘References’ tab at the top.

Footnotes always appear after the quote or paraphrase they relate to. MHRA generally recommends placing footnote numbers at the end of the sentence, immediately after any closing punctuation, like this. 12

In situations where this might be awkward or misleading, such as a long sentence containing multiple quotations, footnotes can also be placed at the end of a clause mid-sentence, like this; 13 note that they still come after any punctuation.

When a source has two or three authors, name all of them in your MHRA references . When there are four or more, use only the first name, followed by ‘and others’:

Note that in the bibliography, only the author listed first has their name inverted. The names of additional authors and those of translators or editors are written normally.

A citation should appear wherever you use information or ideas from a source, whether by quoting or paraphrasing its content.

In Vancouver style , you have some flexibility about where the citation number appears in the sentence – usually directly after mentioning the author’s name is best, but simply placing it at the end of the sentence is an acceptable alternative, as long as it’s clear what it relates to.

In Vancouver style , when you refer to a source with multiple authors in your text, you should only name the first author followed by ‘et al.’. This applies even when there are only two authors.

In your reference list, include up to six authors. For sources with seven or more authors, list the first six followed by ‘et al.’.

The main difference is in terms of scale – a dissertation is usually much longer than the other essays you complete during your degree.

Another key difference is that you are given much more independence when working on a dissertation. You choose your own dissertation topic , and you have to conduct the research and write the dissertation yourself (with some assistance from your supervisor).

Dissertation word counts vary widely across different fields, institutions, and levels of education:

• An undergraduate dissertation is typically 8,000–15,000 words
• A master’s dissertation is typically 12,000–50,000 words
• A PhD thesis is typically book-length: 70,000–100,000 words

However, none of these are strict guidelines – your word count may be lower or higher than the numbers stated here. Always check the guidelines provided by your university to determine how long your own dissertation should be.

At the bachelor’s and master’s levels, the dissertation is usually the main focus of your final year. You might work on it (alongside other classes) for the entirety of the final year, or for the last six months. This includes formulating an idea, doing the research, and writing up.

A PhD thesis takes a longer time, as the thesis is the main focus of the degree. A PhD thesis might be being formulated and worked on for the whole four years of the degree program. The writing process alone can take around 18 months.

References should be included in your text whenever you use words, ideas, or information from a source. A source can be anything from a book or journal article to a website or YouTube video.

If you don’t acknowledge your sources, you can get in trouble for plagiarism .

Your university should tell you which referencing style to follow. If you’re unsure, check with a supervisor. Commonly used styles include:

• Harvard referencing , the most commonly used style in UK universities.
• MHRA , used in humanities subjects.
• APA , used in the social sciences.
• Vancouver , used in biomedicine.
• OSCOLA , used in law.

Your university may have its own referencing style guide.

If you are allowed to choose which style to follow, we recommend Harvard referencing, as it is a straightforward and widely used style.

To avoid plagiarism , always include a reference when you use words, ideas or information from a source. This shows that you are not trying to pass the work of others off as your own.

You must also properly quote or paraphrase the source. If you’re not sure whether you’ve done this correctly, you can use the Scribbr Plagiarism Checker to find and correct any mistakes.

In Harvard style , when you quote directly from a source that includes page numbers, your in-text citation must include a page number. For example: (Smith, 2014, p. 33).

You can also include page numbers to point the reader towards a passage that you paraphrased . If you refer to the general ideas or findings of the source as a whole, you don’t need to include a page number.

When you want to use a quote but can’t access the original source, you can cite it indirectly. In the in-text citation , first mention the source you want to refer to, and then the source in which you found it. For example:

It’s advisable to avoid indirect citations wherever possible, because they suggest you don’t have full knowledge of the sources you’re citing. Only use an indirect citation if you can’t reasonably gain access to the original source.

In Harvard style referencing , to distinguish between two sources by the same author that were published in the same year, you add a different letter after the year for each source:

• (Smith, 2019a)
• (Smith, 2019b)

Add ‘a’ to the first one you cite, ‘b’ to the second, and so on. Do the same in your bibliography or reference list .

To create a hanging indent for your bibliography or reference list :

• Highlight all the entries
• Click on the arrow in the bottom-right corner of the ‘Paragraph’ tab in the top menu.
• In the pop-up window, under ‘Special’ in the ‘Indentation’ section, use the drop-down menu to select ‘Hanging’.
• Then close the window with ‘OK’.

Though the terms are sometimes used interchangeably, there is a difference in meaning:

• A reference list only includes sources cited in the text – every entry corresponds to an in-text citation .
• A bibliography also includes other sources which were consulted during the research but not cited.

It’s important to assess the reliability of information found online. Look for sources from established publications and institutions with expertise (e.g. peer-reviewed journals and government agencies).

The CRAAP test (currency, relevance, authority, accuracy, purpose) can aid you in assessing sources, as can our list of credible sources . You should generally avoid citing websites like Wikipedia that can be edited by anyone – instead, look for the original source of the information in the “References” section.

You can generally omit page numbers in your in-text citations of online sources which don’t have them. But when you quote or paraphrase a specific passage from a particularly long online source, it’s useful to find an alternate location marker.

For text-based sources, you can use paragraph numbers (e.g. ‘para. 4’) or headings (e.g. ‘under “Methodology”’). With video or audio sources, use a timestamp (e.g. ‘10:15’).

In the acknowledgements of your thesis or dissertation, you should first thank those who helped you academically or professionally, such as your supervisor, funders, and other academics.

Then you can include personal thanks to friends, family members, or anyone else who supported you during the process.

Yes, it’s important to thank your supervisor(s) in the acknowledgements section of your thesis or dissertation .

Even if you feel your supervisor did not contribute greatly to the final product, you still should acknowledge them, if only for a very brief thank you. If you do not include your supervisor, it may be seen as a snub.

The acknowledgements are generally included at the very beginning of your thesis or dissertation, directly after the title page and before the abstract .

In a thesis or dissertation, the acknowledgements should usually be no longer than one page. There is no minimum length.

You may acknowledge God in your thesis or dissertation acknowledgements , but be sure to follow academic convention by also thanking the relevant members of academia, as well as family, colleagues, and friends who helped you.

All level 1 and 2 headings should be included in your table of contents . That means the titles of your chapters and the main sections within them.

The contents should also include all appendices and the lists of tables and figures, if applicable, as well as your reference list .

Do not include the acknowledgements or abstract   in the table of contents.

To automatically insert a table of contents in Microsoft Word, follow these steps:

• Apply heading styles throughout the document.
• In the references section in the ribbon, locate the Table of Contents group.
• Click the arrow next to the Table of Contents icon and select Custom Table of Contents.
• Select which levels of headings you would like to include in the table of contents.

Make sure to update your table of contents if you move text or change headings. To update, simply right click and select Update Field.

The table of contents in a thesis or dissertation always goes between your abstract and your introduction.

An abbreviation is a shortened version of an existing word, such as Dr for Doctor. In contrast, an acronym uses the first letter of each word to create a wholly new word, such as UNESCO (an acronym for the United Nations Educational, Scientific and Cultural Organization).

Your dissertation sometimes contains a list of abbreviations .

As a rule of thumb, write the explanation in full the first time you use an acronym or abbreviation. You can then proceed with the shortened version. However, if the abbreviation is very common (like UK or PC), then you can just use the abbreviated version straight away.

Be sure to add each abbreviation in your list of abbreviations !

If you only used a few abbreviations in your thesis or dissertation, you don’t necessarily need to include a list of abbreviations .

If your abbreviations are numerous, or if you think they won’t be known to your audience, it’s never a bad idea to add one. They can also improve readability, minimising confusion about abbreviations unfamiliar to your reader.

A list of abbreviations is a list of all the abbreviations you used in your thesis or dissertation. It should appear at the beginning of your document, immediately after your table of contents . It should always be in alphabetical order.

Fishbone diagrams have a few different names that are used interchangeably, including herringbone diagram, cause-and-effect diagram, and Ishikawa diagram.

These are all ways to refer to the same thing– a problem-solving approach that uses a fish-shaped diagram to model possible root causes of problems and troubleshoot solutions.

Fishbone diagrams (also called herringbone diagrams, cause-and-effect diagrams, and Ishikawa diagrams) are most popular in fields of quality management. They are also commonly used in nursing and healthcare, or as a brainstorming technique for students.

Some synonyms and near synonyms of among include:

• In the company of
• In the middle of
• Surrounded by

Some synonyms and near synonyms of between  include:

• In the space separating
• In the time separating

In spite of   is a preposition used to mean ‘ regardless of ‘, ‘notwithstanding’, or ‘even though’.

It’s always used in a subordinate clause to contrast with the information given in the main clause of a sentence (e.g., ‘Amy continued to watch TV, in spite of the time’).

Despite   is a preposition used to mean ‘ regardless of ‘, ‘notwithstanding’, or ‘even though’.

It’s used in a subordinate clause to contrast with information given in the main clause of a sentence (e.g., ‘Despite the stress, Joe loves his job’).

‘Log in’ is a phrasal verb meaning ‘connect to an electronic device, system, or app’. The preposition ‘to’ is often used directly after the verb; ‘in’ and ‘to’ should be written as two separate words (e.g., ‘ log in to the app to update privacy settings’).

‘Log into’ is sometimes used instead of ‘log in to’, but this is generally considered incorrect (as is ‘login to’).

Some synonyms and near synonyms of ensure include:

• Make certain

Some synonyms and near synonyms of assure  include:

Rest assured is an expression meaning ‘you can be certain’ (e.g., ‘Rest assured, I will find your cat’). ‘Assured’ is the adjectival form of the verb assure , meaning ‘convince’ or ‘persuade’.

Some synonyms and near synonyms for council include:

There are numerous synonyms and near synonyms for the two meanings of counsel :

AI writing tools can be used to perform a variety of tasks.

Generative AI writing tools (like ChatGPT ) generate text based on human inputs and can be used for interactive learning, to provide feedback, or to generate research questions or outlines.

These tools can also be used to paraphrase or summarise text or to identify grammar and punctuation mistakes. Y ou can also use Scribbr’s free paraphrasing tool , summarising tool , and grammar checker , which are designed specifically for these purposes.

Using AI writing tools (like ChatGPT ) to write your essay is usually considered plagiarism and may result in penalisation, unless it is allowed by your university. Text generated by AI tools is based on existing texts and therefore cannot provide unique insights. Furthermore, these outputs sometimes contain factual inaccuracies or grammar mistakes.

However, AI writing tools can be used effectively as a source of feedback and inspiration for your writing (e.g., to generate research questions ). Other AI tools, like grammar checkers, can help identify and eliminate grammar and punctuation mistakes to enhance your writing.

The Scribbr Knowledge Base is a collection of free resources to help you succeed in academic research, writing, and citation. Every week, we publish helpful step-by-step guides, clear examples, simple templates, engaging videos, and more.

The Knowledge Base is for students at all levels. Whether you’re writing your first essay, working on your bachelor’s or master’s dissertation, or getting to grips with your PhD research, we’ve got you covered.

As well as the Knowledge Base, Scribbr provides many other tools and services to support you in academic writing and citation:

• Create your citations and manage your reference list with our free Reference Generators in APA and MLA style.
• Scan your paper for in-text citation errors and inconsistencies with our innovative APA Citation Checker .
• Avoid accidental plagiarism with our reliable Plagiarism Checker .
• Polish your writing and get feedback on structure and clarity with our Proofreading & Editing services .

Yes! We’re happy for educators to use our content, and we’ve even adapted some of our articles into ready-made lecture slides .

You are free to display, distribute, and adapt Scribbr materials in your classes or upload them in private learning environments like Blackboard. We only ask that you credit Scribbr for any content you use.

We’re always striving to improve the Knowledge Base. If you have an idea for a topic we should cover, or you notice a mistake in any of our articles, let us know by emailing [email protected] .

The consequences of plagiarism vary depending on the type of plagiarism and the context in which it occurs. For example, submitting a whole paper by someone else will have the most severe consequences, while accidental citation errors are considered less serious.

If you’re a student, then you might fail the course, be suspended or expelled, or be obligated to attend a workshop on plagiarism. It depends on whether it’s your first offence or you’ve done it before.

As an academic or professional, plagiarising seriously damages your reputation. You might also lose your research funding or your job, and you could even face legal consequences for copyright infringement.

Paraphrasing without crediting the original author is a form of plagiarism , because you’re presenting someone else’s ideas as if they were your own.

However, paraphrasing is not plagiarism if you correctly reference the source . This means including an in-text referencing and a full reference , formatted according to your required citation style (e.g., Harvard , Vancouver ).

As well as referencing your source, make sure that any paraphrased text is completely rewritten in your own words.

Accidental plagiarism is one of the most common examples of plagiarism . Perhaps you forgot to cite a source, or paraphrased something a bit too closely. Maybe you can’t remember where you got an idea from, and aren’t totally sure if it’s original or not.

These all count as plagiarism, even though you didn’t do it on purpose. When in doubt, make sure you’re citing your sources . Also consider running your work through a plagiarism checker tool prior to submission, which work by using advanced database software to scan for matches between your text and existing texts.

Scribbr’s Plagiarism Checker takes less than 10 minutes and can help you turn in your paper with confidence.

The accuracy depends on the plagiarism checker you use. Per our in-depth research , Scribbr is the most accurate plagiarism checker. Many free plagiarism checkers fail to detect all plagiarism or falsely flag text as plagiarism.

Plagiarism checkers work by using advanced database software to scan for matches between your text and existing texts. Their accuracy is determined by two factors: the algorithm (which recognises the plagiarism) and the size of the database (with which your document is compared).

To avoid plagiarism when summarising an article or other source, follow these two rules:

• Write the summary entirely in your own words by   paraphrasing the author’s ideas.
• Reference the source with an in-text citation and a full reference so your reader can easily find the original text.

Plagiarism can be detected by your professor or readers if the tone, formatting, or style of your text is different in different parts of your paper, or if they’re familiar with the plagiarised source.

Many universities also use   plagiarism detection software like Turnitin’s, which compares your text to a large database of other sources, flagging any similarities that come up.

It can be easier than you think to commit plagiarism by accident. Consider using a   plagiarism checker prior to submitting your essay to ensure you haven’t missed any citations.

Some examples of plagiarism include:

• Copying and pasting a Wikipedia article into the body of an assignment
• Quoting a source without including a citation
• Not paraphrasing a source properly (e.g. maintaining wording too close to the original)
• Forgetting to cite the source of an idea

The most surefire way to   avoid plagiarism is to always cite your sources . When in doubt, cite!

Global plagiarism means taking an entire work written by someone else and passing it off as your own. This can include getting someone else to write an essay or assignment for you, or submitting a text you found online as your own work.

Global plagiarism is one of the most serious types of plagiarism because it involves deliberately and directly lying about the authorship of a work. It can have severe consequences for students and professionals alike.

Verbatim plagiarism means copying text from a source and pasting it directly into your own document without giving proper credit.

If the structure and the majority of the words are the same as in the original source, then you are committing verbatim plagiarism. This is the case even if you delete a few words or replace them with synonyms.

If you want to use an author’s exact words, you need to quote the original source by putting the copied text in quotation marks and including an   in-text citation .

Patchwork plagiarism , also called mosaic plagiarism, means copying phrases, passages, or ideas from various existing sources and combining them to create a new text. This includes slightly rephrasing some of the content, while keeping many of the same words and the same structure as the original.

While this type of plagiarism is more insidious than simply copying and pasting directly from a source, plagiarism checkers like Turnitin’s can still easily detect it.

To avoid plagiarism in any form, remember to reference your sources .

Yes, reusing your own work without citation is considered self-plagiarism . This can range from resubmitting an entire assignment to reusing passages or data from something you’ve handed in previously.

Self-plagiarism often has the same consequences as other types of plagiarism . If you want to reuse content you wrote in the past, make sure to check your university’s policy or consult your professor.

If you are reusing content or data you used in a previous assignment, make sure to cite yourself. You can cite yourself the same way you would cite any other source: simply follow the directions for the citation style you are using.

Keep in mind that reusing prior content can be considered self-plagiarism , so make sure you ask your instructor or consult your university’s handbook prior to doing so.

Most institutions have an internal database of previously submitted student assignments. Turnitin can check for self-plagiarism by comparing your paper against this database. If you’ve reused parts of an assignment you already submitted, it will flag any similarities as potential plagiarism.

Online plagiarism checkers don’t have access to your institution’s database, so they can’t detect self-plagiarism of unpublished work. If you’re worried about accidentally self-plagiarising, you can use Scribbr’s Self-Plagiarism Checker to upload your unpublished documents and check them for similarities.

Plagiarism has serious consequences and can be illegal in certain scenarios.

While most of the time plagiarism in an undergraduate setting is not illegal, plagiarism or self-plagiarism in a professional academic setting can lead to legal action, including copyright infringement and fraud. Many scholarly journals do not allow you to submit the same work to more than one journal, and if you do not credit a coauthor, you could be legally defrauding them.

Even if you aren’t breaking the law, plagiarism can seriously impact your academic career. While the exact consequences of plagiarism vary by institution and severity, common consequences include a lower grade, automatically failing a course, academic suspension or probation, and even expulsion.

Self-plagiarism means recycling work that you’ve previously published or submitted as an assignment. It’s considered academic dishonesty to present something as brand new when you’ve already gotten credit and perhaps feedback for it in the past.

If you want to refer to ideas or data from previous work, be sure to cite yourself.

Academic integrity means being honest, ethical, and thorough in your academic work. To maintain academic integrity, you should avoid misleading your readers about any part of your research and refrain from offences like plagiarism and contract cheating, which are examples of academic misconduct.

Academic dishonesty refers to deceitful or misleading behavior in an academic setting. Academic dishonesty can occur intentionally or unintentionally, and it varies in severity.

It can encompass paying for a pre-written essay, cheating on an exam, or committing plagiarism . It can also include helping others cheat, copying a friend’s homework answers, or even pretending to be sick to miss an exam.

Academic dishonesty doesn’t just occur in a classroom setting, but also in research and other academic-adjacent fields.

Consequences of academic dishonesty depend on the severity of the offence and your institution’s policy. They can range from a warning for a first offence to a failing grade in a course to expulsion from your university.

For those in certain fields, such as nursing, engineering, or lab sciences, not learning fundamentals properly can directly impact the health and safety of others. For those working in academia or research, academic dishonesty impacts your professional reputation, leading others to doubt your future work.

Academic dishonesty can be intentional or unintentional, ranging from something as simple as claiming to have read something you didn’t to copying your neighbour’s answers on an exam.

You can commit academic dishonesty with the best of intentions, such as helping a friend cheat on a paper. Severe academic dishonesty can include buying a pre-written essay or the answers to a multiple-choice test, or falsifying a medical emergency to avoid taking a final exam.

Plagiarism means presenting someone else’s work as your own without giving proper credit to the original author. In academic writing, plagiarism involves using words, ideas, or information from a source without including a citation .

Plagiarism can have serious consequences , even when it’s done accidentally. To avoid plagiarism, it’s important to keep track of your sources and cite them correctly.

Common knowledge does not need to be cited. However, you should be extra careful when deciding what counts as common knowledge.

Common knowledge encompasses information that the average educated reader would accept as true without needing the extra validation of a source or citation.

Common knowledge should be widely known, undisputed, and easily verified. When in doubt, always cite your sources.

Most online plagiarism checkers only have access to public databases, whose software doesn’t allow you to compare two documents for plagiarism.

However, in addition to our Plagiarism Checker , Scribbr also offers an Self-Plagiarism Checker . This is an add-on tool that lets you compare your paper with unpublished or private documents. This way you can rest assured that you haven’t unintentionally plagiarised or self-plagiarised .

Compare two sources for plagiarism

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analysed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analysed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualise your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analysed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analysing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Triangulation can help:

• Reduce bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labour-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

In multistage sampling , you can use probability or non-probability sampling methods.

For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method .

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction, and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa.

The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.

The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment, and situation effect.

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity, and criterion validity to achieve construct validity.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalisation: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation)

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

On graphs, the explanatory variable is conventionally placed on the x -axis, while the response variable is placed on the y -axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term ‘ explanatory variable ‘ is sometimes preferred over ‘ independent variable ‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

There are 4 main types of extraneous variables :

• Demand characteristics : Environmental cues that encourage participants to conform to researchers’ expectations
• Experimenter effects : Unintentional actions by researchers that influence study outcomes
• Situational variables : Eenvironmental variables that alter participants’ behaviours
• Participant variables : Any characteristic or aspect of a participant’s background that could affect study results

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomisation , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .

• The type of cola – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of cola.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g., the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g., water volume or weight).

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
• Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order.
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups . Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with ‘yes’ or ‘no’ (questions that start with ‘why’ or ‘how’ are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment
• Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as ‘people watching’ with a purpose.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

You can use several tactics to minimise observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure inter-rater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardise your observation procedures to make sure they are structured and clear.

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimise or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.

It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps:

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or
• Send it onward to the selected peer reviewer(s)
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.

For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The two main types of social desirability bias are:

• Self-deceptive enhancement (self-deception): The tendency to see oneself in a favorable light without realizing it.
• Impression managemen t (other-deception): The tendency to inflate one’s abilities or achievement in order to make a good impression on other people.

Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias .

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection , using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extra-marital affairs)

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. 

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research . As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research .

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalysing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ).

On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

• Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time
• Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test

Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :

• Construct validity : Does the test measure the construct it was designed to measure?
• Face validity : Does the test appear to be suitable for its objectives ?
• Content validity : Does the test cover all relevant parts of the construct it aims to measure.
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs .

On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.

Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods.

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

The Scribbr Reference Generator is developed using the open-source Citation Style Language (CSL) project and Frank Bennett’s citeproc-js . It’s the same technology used by dozens of other popular citation tools, including Mendeley and Zotero.

You can find all the citation styles and locales used in the Scribbr Reference Generator in our publicly accessible repository on Github .

To paraphrase effectively, don’t just take the original sentence and swap out some of the words for synonyms. Instead, try:

• Reformulating the sentence (e.g., change active to passive , or start from a different point)
• Combining information from multiple sentences into one
• Leaving out information from the original that isn’t relevant to your point
• Using synonyms where they don’t distort the meaning

The main point is to ensure you don’t just copy the structure of the original text, but instead reformulate the idea in your own words.

Plagiarism means using someone else’s words or ideas and passing them off as your own. Paraphrasing means putting someone else’s ideas into your own words.

So when does paraphrasing count as plagiarism?

• Paraphrasing is plagiarism if you don’t properly credit the original author.
• Paraphrasing is plagiarism if your text is too close to the original wording (even if you cite the source). If you directly copy a sentence or phrase, you should quote it instead.
• Paraphrasing  is not plagiarism if you put the author’s ideas completely into your own words and properly reference the source .

To present information from other sources in academic writing , it’s best to paraphrase in most cases. This shows that you’ve understood the ideas you’re discussing and incorporates them into your text smoothly.

It’s appropriate to quote when:

• Changing the phrasing would distort the meaning of the original text
• You want to discuss the author’s language choices (e.g., in literary analysis )
• You’re presenting a precise definition
• You’re looking in depth at a specific claim

A quote is an exact copy of someone else’s words, usually enclosed in quotation marks and credited to the original author or speaker.

Every time you quote a source , you must include a correctly formatted in-text citation . This looks slightly different depending on the citation style .

For example, a direct quote in APA is cited like this: ‘This is a quote’ (Streefkerk, 2020, p. 5).

Every in-text citation should also correspond to a full reference at the end of your paper.

In scientific subjects, the information itself is more important than how it was expressed, so quoting should generally be kept to a minimum. In the arts and humanities, however, well-chosen quotes are often essential to a good paper.

In social sciences, it varies. If your research is mainly quantitative , you won’t include many quotes, but if it’s more qualitative , you may need to quote from the data you collected .

As a general guideline, quotes should take up no more than 5–10% of your paper. If in doubt, check with your instructor or supervisor how much quoting is appropriate in your field.

If you’re quoting from a text that paraphrases or summarises other sources and cites them in parentheses , APA  recommends retaining the citations as part of the quote:

• Smith states that ‘the literature on this topic (Jones, 2015; Sill, 2019; Paulson, 2020) shows no clear consensus’ (Smith, 2019, p. 4).

Footnote or endnote numbers that appear within quoted text should be omitted.

If you want to cite an indirect source (one you’ve only seen quoted in another source), either locate the original source or use the phrase ‘as cited in’ in your citation.

A block quote is a long quote formatted as a separate ‘block’ of text. Instead of using quotation marks , you place the quote on a new line, and indent the entire quote to mark it apart from your own words.

APA uses block quotes for quotes that are 40 words or longer.

A credible source should pass the CRAAP test  and follow these guidelines:

• The information should be up to date and current.
• The author and publication should be a trusted authority on the subject you are researching.
• The sources the author cited should be easy to find, clear, and unbiased.
• For a web source, the URL and layout should signify that it is trustworthy.

Common examples of primary sources include interview transcripts , photographs, novels, paintings, films, historical documents, and official statistics.

Anything you directly analyze or use as first-hand evidence can be a primary source, including qualitative or quantitative data that you collected yourself.

Common examples of secondary sources include academic books, journal articles , reviews, essays , and textbooks.

Anything that summarizes, evaluates or interprets primary sources can be a secondary source. If a source gives you an overview of background information or presents another researcher’s ideas on your topic, it is probably a secondary source.

To determine if a source is primary or secondary, ask yourself:

• Was the source created by someone directly involved in the events you’re studying (primary), or by another researcher (secondary)?
• Does the source provide original information (primary), or does it summarize information from other sources (secondary)?
• Are you directly analyzing the source itself (primary), or only using it for background information (secondary)?

Some types of sources are nearly always primary: works of art and literature, raw statistical data, official documents and records, and personal communications (e.g. letters, interviews ). If you use one of these in your research, it is probably a primary source.

Primary sources are often considered the most credible in terms of providing evidence for your argument, as they give you direct evidence of what you are researching. However, it’s up to you to ensure the information they provide is reliable and accurate.

Always make sure to properly cite your sources to avoid plagiarism .

A fictional movie is usually a primary source. A documentary can be either primary or secondary depending on the context.

If you are directly analysing some aspect of the movie itself – for example, the cinematography, narrative techniques, or social context – the movie is a primary source.

If you use the movie for background information or analysis about your topic – for example, to learn about a historical event or a scientific discovery – the movie is a secondary source.

Whether it’s primary or secondary, always properly cite the movie in the citation style you are using. Learn how to create an MLA movie citation or an APA movie citation .

Articles in newspapers and magazines can be primary or secondary depending on the focus of your research.

In historical studies, old articles are used as primary sources that give direct evidence about the time period. In social and communication studies, articles are used as primary sources to analyse language and social relations (for example, by conducting content analysis or discourse analysis ).

If you are not analysing the article itself, but only using it for background information or facts about your topic, then the article is a secondary source.

In academic writing , there are three main situations where quoting is the best choice:

• To analyse the author’s language (e.g., in a literary analysis essay )
• To give evidence from primary sources
• To accurately present a precise definition or argument

Don’t overuse quotes; your own voice should be dominant. If you just want to provide information from a source, it’s usually better to paraphrase or summarise .

Your list of tables and figures should go directly after your table of contents in your thesis or dissertation.

Lists of figures and tables are often not required, and they aren’t particularly common. They specifically aren’t required for APA Style, though you should be careful to follow their other guidelines for figures and tables .

If you have many figures and tables in your thesis or dissertation, include one may help you stay organised. Your educational institution may require them, so be sure to check their guidelines.

Copyright information can usually be found wherever the table or figure was published. For example, for a diagram in a journal article , look on the journal’s website or the database where you found the article. Images found on sites like Flickr are listed with clear copyright information.

If you find that permission is required to reproduce the material, be sure to contact the author or publisher and ask for it.

A list of figures and tables compiles all of the figures and tables that you used in your thesis or dissertation and displays them with the page number where they can be found.

APA doesn’t require you to include a list of tables or a list of figures . However, it is advisable to do so if your text is long enough to feature a table of contents and it includes a lot of tables and/or figures .

A list of tables and list of figures appear (in that order) after your table of contents, and are presented in a similar way.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. Your glossary only needs to include terms that your reader may not be familiar with, and is intended to enhance their understanding of your work.

Definitional terms often fall into the category of common knowledge , meaning that they don’t necessarily have to be cited. This guidance can apply to your thesis or dissertation glossary as well.

However, if you’d prefer to cite your sources , you can follow guidance for citing dictionary entries in MLA or APA style for your glossary.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, an index is a list of the contents of your work organised by page number.

Glossaries are not mandatory, but if you use a lot of technical or field-specific terms, it may improve readability to add one to your thesis or dissertation. Your educational institution may also require them, so be sure to check their specific guidelines.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, dictionaries are more general collections of words.

The title page of your thesis or dissertation should include your name, department, institution, degree program, and submission date.

The title page of your thesis or dissertation goes first, before all other content or lists that you may choose to include.

Usually, no title page is needed in an MLA paper . A header is generally included at the top of the first page instead. The exceptions are when:

• Your instructor requires one, or
• Your paper is a group project

In those cases, you should use a title page instead of a header, listing the same information but on a separate page.

When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organise your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

Generally, an outline contains information on the different sections included in your thesis or dissertation, such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review, research methods, avenues for future research, etc.)

While a theoretical framework describes the theoretical underpinnings of your work based on existing research, a conceptual framework allows you to draw your own conclusions, mapping out the variables you may use in your study and the interplay between them.

A literature review and a theoretical framework are not the same thing and cannot be used interchangeably. While a theoretical framework describes the theoretical underpinnings of your work, a literature review critically evaluates existing research relating to your topic. You’ll likely need both in your dissertation .

A theoretical framework can sometimes be integrated into a  literature review chapter , but it can also be included as its own chapter or section in your dissertation . As a rule of thumb, if your research involves dealing with a lot of complex theories, it’s a good idea to include a separate theoretical framework chapter.

An abstract is a concise summary of an academic text (such as a journal article or dissertation ). It serves two main purposes:

• To help potential readers determine the relevance of your paper for their own research.
• To communicate your key findings to those who don’t have time to read the whole paper.

Abstracts are often indexed along with keywords on academic databases, so they make your work more easily findable. Since the abstract is the first thing any reader sees, it’s important that it clearly and accurately summarises the contents of your paper.

The abstract is the very last thing you write. You should only write it after your research is complete, so that you can accurately summarize the entirety of your thesis or paper.

Avoid citing sources in your abstract . There are two reasons for this:

• The abstract should focus on your original research, not on the work of others.
• The abstract should be self-contained and fully understandable without reference to other sources.

There are some circumstances where you might need to mention other sources in an abstract: for example, if your research responds directly to another study or focuses on the work of a single theorist. In general, though, don’t include citations unless absolutely necessary.

The abstract appears on its own page, after the title page and acknowledgements but before the table of contents .

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

A noun is a word that represents a person, thing, concept, or place (e.g., ‘John’, ‘house’, ‘affinity’, ‘river’). Most sentences contain at least one noun or pronoun .

Nouns are often, but not always, preceded by an article (‘the’, ‘a’, or ‘an’) and/or another determiner such as an adjective.

There are many ways to categorize nouns into various types, and the same noun can fall into multiple categories or even change types depending on context.

Some of the main types of nouns are:

• Common nouns and proper nouns
• Countable and uncountable nouns
• Concrete and abstract nouns
• Collective nouns
• Possessive nouns
• Attributive nouns
• Appositive nouns
• Generic nouns

Pronouns are words like ‘I’, ‘she’, and ‘they’ that are used in a similar way to nouns . They stand in for a noun that has already been mentioned or refer to yourself and other people.

Pronouns can function just like nouns as the head of a noun phrase and as the subject or object of a verb. However, pronouns change their forms (e.g., from ‘I’ to ‘me’) depending on the grammatical context they’re used in, whereas nouns usually don’t.

Common nouns are words for types of things, people, and places, such as ‘dog’, ‘professor’, and ‘city’. They are not capitalised and are typically used in combination with articles and other determiners.

Proper nouns are words for specific things, people, and places, such as ‘Max’, ‘Dr Prakash’, and ‘London’. They are always capitalised and usually aren’t combined with articles and other determiners.

A proper adjective is an adjective that was derived from a proper noun and is therefore capitalised .

Proper adjectives include words for nationalities, languages, and ethnicities (e.g., ‘Japanese’, ‘Inuit’, ‘French’) and words derived from people’s names (e.g., ‘Bayesian’, ‘Orwellian’).

The names of seasons (e.g., ‘spring’) are treated as common nouns in English and therefore not capitalised . People often assume they are proper nouns, but this is an error.

The names of days and months, however, are capitalised since they’re treated as proper nouns in English (e.g., ‘Wednesday’, ‘January’).

No, as a general rule, academic concepts, disciplines, theories, models, etc. are treated as common nouns , not proper nouns , and therefore not capitalised . For example, ‘five-factor model of personality’ or ‘analytic philosophy’.

However, proper nouns that appear within the name of an academic concept (such as the name of the inventor) are capitalised as usual. For example, ‘Darwin’s theory of evolution’ or ‘ Student’s t table ‘.

Collective nouns are most commonly treated as singular (e.g., ‘the herd is grazing’), but usage differs between US and UK English :

• In US English, it’s standard to treat all collective nouns as singular, even when they are plural in appearance (e.g., ‘The Rolling Stones is …’). Using the plural form is usually seen as incorrect.
• In UK English, collective nouns can be treated as singular or plural depending on context. It’s quite common to use the plural form, especially when the noun looks plural (e.g., ‘The Rolling Stones are …’).

The plural of “crisis” is “crises”. It’s a loanword from Latin and retains its original Latin plural noun form (similar to “analyses” and “bases”). It’s wrong to write “crisises”.

For example, you might write “Several crises destabilized the regime.”

Normally, the plural of “fish” is the same as the singular: “fish”. It’s one of a group of irregular plural nouns in English that are identical to the corresponding singular nouns (e.g., “moose”, “sheep”). For example, you might write “The fish scatter as the shark approaches.”

If you’re referring to several species of fish, though, the regular plural “fishes” is often used instead. For example, “The aquarium contains many different fishes , including trout and carp.”

The correct plural of “octopus” is “octopuses”.

People often write “octopi” instead because they assume that the plural noun is formed in the same way as Latin loanwords such as “fungus/fungi”. But “octopus” actually comes from Greek, where its original plural is “octopodes”. In English, it instead has the regular plural form “octopuses”.

For example, you might write “There are four octopuses in the aquarium.”

The plural of “moose” is the same as the singular: “moose”. It’s one of a group of plural nouns in English that are identical to the corresponding singular nouns. So it’s wrong to write “mooses”.

For example, you might write “There are several moose in the forest.”

Bias in research affects the validity and reliability of your findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated.

Observer bias occurs when the researcher’s assumptions, views, or preconceptions influence what they see and record in a study, while actor–observer bias refers to situations where respondents attribute internal factors (e.g., bad character) to justify other’s behaviour and external factors (difficult circumstances) to justify the same behaviour in themselves.

Response bias is a general term used to describe a number of different conditions or factors that cue respondents to provide inaccurate or false answers during surveys or interviews . These factors range from the interviewer’s perceived social position or appearance to the the phrasing of questions in surveys.

Nonresponse bias occurs when the people who complete a survey are different from those who did not, in ways that are relevant to the research topic. Nonresponse can happen either because people are not willing or not able to participate.

In research, demand characteristics are cues that might indicate the aim of a study to participants. These cues can lead to participants changing their behaviors or responses based on what they think the research is about.

Demand characteristics are common problems in psychology experiments and other social science studies because they can bias your research findings.

Demand characteristics are a type of extraneous variable that can affect the outcomes of the study. They can invalidate studies by providing an alternative explanation for the results.

These cues may nudge participants to consciously or unconsciously change their responses, and they pose a threat to both internal and external validity . You can’t be sure that your independent variable manipulation worked, or that your findings can be applied to other people or settings.

You can control demand characteristics by taking a few precautions in your research design and materials.

Use these measures:

• Deception: Hide the purpose of the study from participants
• Between-groups design : Give each participant only one independent variable treatment
• Double-blind design : Conceal the assignment of groups from participants and yourself
• Implicit measures: Use indirect or hidden measurements for your variables

Some attrition is normal and to be expected in research. However, the type of attrition is important because systematic research bias can distort your findings. Attrition bias can lead to inaccurate results because it affects internal and/or external validity .

To avoid attrition bias , applying some of these measures can help you reduce participant dropout (attrition) by making it easy and appealing for participants to stay.

• Provide compensation (e.g., cash or gift cards) for attending every session
• Minimise the number of follow-ups as much as possible
• Make all follow-ups brief, flexible, and convenient for participants
• Send participants routine reminders to schedule follow-ups
• Recruit more participants than you need for your sample (oversample)
• Maintain detailed contact information so you can get in touch with participants even if they move

If you have a small amount of attrition bias , you can use a few statistical methods to try to make up for this research bias .

Multiple imputation involves using simulations to replace the missing data with likely values. Alternatively, you can use sample weighting to make up for the uneven balance of participants in your sample.

Placebos are used in medical research for new medication or therapies, called clinical trials. In these trials some people are given a placebo, while others are given the new medication being tested.

The purpose is to determine how effective the new medication is: if it benefits people beyond a predefined threshold as compared to the placebo, it’s considered effective.

Although there is no definite answer to what causes the placebo effect , researchers propose a number of explanations such as the power of suggestion, doctor-patient interaction, classical conditioning, etc.

Belief bias and confirmation bias are both types of cognitive bias that impact our judgment and decision-making.

Confirmation bias relates to how we perceive and judge evidence. We tend to seek out and prefer information that supports our preexisting beliefs, ignoring any information that contradicts those beliefs.

Belief bias describes the tendency to judge an argument based on how plausible the conclusion seems to us, rather than how much evidence is provided to support it during the course of the argument.

Positivity bias is phenomenon that occurs when a person judges individual members of a group positively, even when they have negative impressions or judgments of the group as a whole. Positivity bias is closely related to optimism bias , or the e xpectation that things will work out well, even if rationality suggests that problems are inevitable in life.

Perception bias is a problem because it prevents us from seeing situations or people objectively. Rather, our expectations, beliefs, or emotions interfere with how we interpret reality. This, in turn, can cause us to misjudge ourselves or others. For example, our prejudices can interfere with whether we perceive people’s faces as friendly or unfriendly.

There are many ways to categorize adjectives into various types. An adjective can fall into one or more of these categories depending on how it is used.

Some of the main types of adjectives are:

• Attributive adjectives
• Predicative adjectives
• Comparative adjectives
• Superlative adjectives
• Coordinate adjectives
• Appositive adjectives
• Compound adjectives
• Participial adjectives
• Proper adjectives
• Denominal adjectives
• Nominal adjectives

Cardinal numbers (e.g., one, two, three) can be placed before a noun to indicate quantity (e.g., one apple). While these are sometimes referred to as ‘numeral adjectives ‘, they are more accurately categorised as determiners or quantifiers.

Proper adjectives are adjectives formed from a proper noun (i.e., the name of a specific person, place, or thing) that are used to indicate origin. Like proper nouns, proper adjectives are always capitalised (e.g., Newtonian, Marxian, African).

The cost of proofreading depends on the type and length of text, the turnaround time, and the level of services required. Most proofreading companies charge per word or page, while freelancers sometimes charge an hourly rate.

For proofreading alone, which involves only basic corrections of typos and formatting mistakes, you might pay as little as £0.01 per word, but in many cases, your text will also require some level of editing , which costs slightly more.

It’s often possible to purchase combined proofreading and editing services and calculate the price in advance based on your requirements.

Then and than are two commonly confused words . In the context of ‘better than’, you use ‘than’ with an ‘a’.

• Julie is better than Jesse.
• I’d rather spend my time with you than with him.
• I understand Eoghan’s point of view better than Claudia’s.

Use to and used to are commonly confused words . In the case of ‘used to do’, the latter (with ‘d’) is correct, since you’re describing an action or state in the past.

• I used to do laundry once a week.
• They used to do each other’s hair.
• We used to do the dishes every day .

There are numerous synonyms and near synonyms for the various meanings of “ favour ”:

There are numerous synonyms and near synonyms for the two meanings of “ favoured ”:

No one (two words) is an indefinite pronoun meaning ‘nobody’. People sometimes mistakenly write ‘noone’, but this is incorrect and should be avoided. ‘No-one’, with a hyphen, is also acceptable in UK English .

Nobody and no one are both indefinite pronouns meaning ‘no person’. They can be used interchangeably (e.g., ‘nobody is home’ means the same as ‘no one is home’).

Some synonyms and near synonyms of  every time include:

• Without exception

‘Everytime’ is sometimes used to mean ‘each time’ or ‘whenever’. However, this is incorrect and should be avoided. The correct phrase is every time   (two words).

Yes, the conjunction because is a compound word , but one with a long history. It originates in Middle English from the preposition “bi” (“by”) and the noun “cause”. Over time, the open compound “bi cause” became the closed compound “because”, which we use today.

Though it’s spelled this way now, the verb “be” is not one of the words that makes up “because”.

Yes, today is a compound word , but a very old one. It wasn’t originally formed from the preposition “to” and the noun “day”; rather, it originates from their Old English equivalents, “tō” and “dæġe”.

In the past, it was sometimes written as a hyphenated compound: “to-day”. But the hyphen is no longer included; it’s always “today” now (“to day” is also wrong).

IEEE citation format is defined by the Institute of Electrical and Electronics Engineers and used in their publications.

It’s also a widely used citation style for students in technical fields like electrical and electronic engineering, computer science, telecommunications, and computer engineering.

An IEEE in-text citation consists of a number in brackets at the relevant point in the text, which points the reader to the right entry in the numbered reference list at the end of the paper. For example, ‘Smith [1] states that …’

A location marker such as a page number is also included within the brackets when needed: ‘Smith [1, p. 13] argues …’

The IEEE reference page consists of a list of references numbered in the order they were cited in the text. The title ‘References’ appears in bold at the top, either left-aligned or centered.

The numbers appear in square brackets on the left-hand side of the page. The reference entries are indented consistently to separate them from the numbers. Entries are single-spaced, with a normal paragraph break between them.

If you cite the same source more than once in your writing, use the same number for all of the IEEE in-text citations for that source, and only include it on the IEEE reference page once. The source is numbered based on the first time you cite it.

For example, the fourth source you cite in your paper is numbered [4]. If you cite it again later, you still cite it as [4]. You can cite different parts of the source each time by adding page numbers [4, p. 15].

A verb is a word that indicates a physical action (e.g., ‘drive’), a mental action (e.g., ‘think’) or a state of being (e.g., ‘exist’). Every sentence contains a verb.

Verbs are almost always used along with a noun or pronoun to describe what the noun or pronoun is doing.

There are many ways to categorize verbs into various types. A verb can fall into one or more of these categories depending on how it is used.

Some of the main types of verbs are:

• Regular verbs
• Irregular verbs
• Transitive verbs
• Intransitive verbs
• Dynamic verbs
• Stative verbs
• Linking verbs
• Auxiliary verbs
• Modal verbs
• Phrasal verbs

Regular verbs are verbs whose simple past and past participle are formed by adding the suffix ‘-ed’ (e.g., ‘walked’).

Irregular verbs are verbs that form their simple past and past participles in some way other than by adding the suffix ‘-ed’ (e.g., ‘sat’).

The indefinite articles a and an are used to refer to a general or unspecified version of a noun (e.g., a house). Which indefinite article you use depends on the pronunciation of the word that follows it.

• A is used for words that begin with a consonant sound (e.g., a bear).
• An is used for words that begin with a vowel sound (e.g., an eagle).

Indefinite articles can only be used with singular countable nouns . Like definite articles, they are a type of determiner .

Editing and proofreading are different steps in the process of revising a text.

Editing comes first, and can involve major changes to content, structure and language. The first stages of editing are often done by authors themselves, while a professional editor makes the final improvements to grammar and style (for example, by improving sentence structure and word choice ).

Proofreading is the final stage of checking a text before it is published or shared. It focuses on correcting minor errors and inconsistencies (for example, in punctuation and capitalization ). Proofreaders often also check for formatting issues, especially in print publishing.

Whether you’re publishing a blog, submitting a research paper , or even just writing an important email, there are a few techniques you can use to make sure it’s error-free:

• Take a break : Set your work aside for at least a few hours so that you can look at it with fresh eyes.
• Proofread a printout : Staring at a screen for too long can cause fatigue – sit down with a pen and paper to check the final version.
• Use digital shortcuts : Take note of any recurring mistakes (for example, misspelling a particular word, switching between US and UK English , or inconsistently capitalizing a term), and use Find and Replace to fix it throughout the document.

If you want to be confident that an important text is error-free, it might be worth choosing a professional proofreading service instead.

There are many different routes to becoming a professional proofreader or editor. The necessary qualifications depend on the field – to be an academic or scientific proofreader, for example, you will need at least a university degree in a relevant subject.

For most proofreading jobs, experience and demonstrated skills are more important than specific qualifications. Often your skills will be tested as part of the application process.

To learn practical proofreading skills, you can choose to take a course with a professional organisation such as the Society for Editors and Proofreaders . Alternatively, you can apply to companies that offer specialised on-the-job training programmes, such as the Scribbr Academy .

Though they’re pronounced the same, there’s a big difference in meaning between its and it’s .

• ‘The cat ate its food’.
• ‘It’s almost Christmas’.

Its and it’s are often confused, but its (without apostrophe) is the possessive form of ‘it’ (e.g., its tail, its argument, its wing). You use ‘its’ instead of ‘his’ and ‘her’ for neuter, inanimate nouns.

Then and than are two commonly confused words with different meanings and grammatical roles.

• Then (pronounced with a short ‘e’ sound) refers to time. It’s often an adverb , but it can also be used as a noun meaning ‘that time’ and as an adjective referring to a previous status.
• Than (pronounced with a short ‘a’ sound) is used for comparisons. Grammatically, it usually functions as a conjunction , but sometimes it’s a preposition .

Use to and used to are commonly confused words . In the case of ‘used to be’, the latter (with ‘d’) is correct, since you’re describing an action or state in the past.

• I used to be the new coworker.
• There used to be 4 cookies left.
• We used to walk to school every day .

A grammar checker is a tool designed to automatically check your text for spelling errors, grammatical issues, punctuation mistakes , and problems with sentence structure . You can check out our analysis of the best free grammar checkers to learn more.

A paraphrasing tool edits your text more actively, changing things whether they were grammatically incorrect or not. It can paraphrase your sentences to make them more concise and readable or for other purposes. You can check out our analysis of the best free paraphrasing tools to learn more.

Some tools available online combine both functions. Others, such as QuillBot , have separate grammar checker and paraphrasing tools. Be aware of what exactly the tool you’re using does to avoid introducing unwanted changes.

Good grammar is the key to expressing yourself clearly and fluently, especially in professional communication and academic writing . Word processors, browsers, and email programs typically have built-in grammar checkers, but they’re quite limited in the kinds of problems they can fix.

If you want to go beyond detecting basic spelling errors, there are many online grammar checkers with more advanced functionality. They can often detect issues with punctuation , word choice, and sentence structure that more basic tools would miss.

Not all of these tools are reliable, though. You can check out our research into the best free grammar checkers to explore the options.

Our research indicates that the best free grammar checker available online is the QuillBot grammar checker .

We tested 10 of the most popular checkers with the same sample text (containing 20 grammatical errors) and found that QuillBot easily outperformed the competition, scoring 18 out of 20, a drastic improvement over the second-place score of 13 out of 20.

It even appeared to outperform the premium versions of other grammar checkers, despite being entirely free.

A teacher’s aide is a person who assists in teaching classes but is not a qualified teacher. Aide is a noun meaning ‘assistant’, so it will always refer to a person.

‘Teacher’s aid’ is incorrect.

A visual aid is an instructional device (e.g., a photo, a chart) that appeals to vision to help you understand written or spoken information. Aid is often placed after an attributive noun or adjective (like ‘visual’) that describes the type of help provided.

‘Visual aide’ is incorrect.

A job aid is an instructional tool (e.g., a checklist, a cheat sheet) that helps you work efficiently. Aid is a noun meaning ‘assistance’. It’s often placed after an adjective or attributive noun (like ‘job’) that describes the specific type of help provided.

‘Job aide’ is incorrect.

There are numerous synonyms for the various meanings of truly :

Yours truly is a phrase used at the end of a formal letter or email. It can also be used (typically in a humorous way) as a pronoun to refer to oneself (e.g., ‘The dinner was cooked by yours truly ‘). The latter usage should be avoided in formal writing.

It’s formed by combining the second-person possessive pronoun ‘yours’ with the adverb ‘ truly ‘.

A pathetic fallacy can be a short phrase or a whole sentence and is often used in novels and poetry. Pathetic fallacies serve multiple purposes, such as:

• Conveying the emotional state of the characters or the narrator
• Creating an atmosphere or set the mood of a scene
• Foreshadowing events to come
• Giving texture and vividness to a piece of writing
• Communicating emotion to the reader in a subtle way, by describing the external world.
• Bringing inanimate objects to life so that they seem more relatable.

AMA citation format is a citation style designed by the American Medical Association. It’s frequently used in the field of medicine.

You may be told to use AMA style for your student papers. You will also have to follow this style if you’re submitting a paper to a journal published by the AMA.

An AMA in-text citation consists of the number of the relevant reference on your AMA reference page , written in superscript 1 at the point in the text where the source is used.

It may also include the page number or range of the relevant material in the source (e.g., the part you quoted 2(p46) ). Multiple sources can be cited at one point, presented as a range or list (with no spaces 3,5–9 ).

An AMA reference usually includes the author’s last name and initials, the title of the source, information about the publisher or the publication it’s contained in, and the publication date. The specific details included, and the formatting, depend on the source type.

References in AMA style are presented in numerical order (numbered by the order in which they were first cited in the text) on your reference page. A source that’s cited repeatedly in the text still only appears once on the reference page.

An AMA in-text citation just consists of the number of the relevant entry on your AMA reference page , written in superscript at the point in the text where the source is referred to.

You don’t need to mention the author of the source in your sentence, but you can do so if you want. It’s not an official part of the citation, but it can be useful as part of a signal phrase introducing the source.

On your AMA reference page , author names are written with the last name first, followed by the initial(s) of their first name and middle name if mentioned.

There’s a space between the last name and the initials, but no space or punctuation between the initials themselves. The names of multiple authors are separated by commas , and the whole list ends in a period, e.g., ‘Andreessen F, Smith PW, Gonzalez E’.

The names of up to six authors should be listed for each source on your AMA reference page , separated by commas . For a source with seven or more authors, you should list the first three followed by ‘ et al’ : ‘Isidore, Gilbert, Gunvor, et al’.

In the text, mentioning author names is optional (as they aren’t an official part of AMA in-text citations ). If you do mention them, though, you should use the first author’s name followed by ‘et al’ when there are three or more : ‘Isidore et al argue that …’

Note that according to AMA’s rather minimalistic punctuation guidelines, there’s no period after ‘et al’ unless it appears at the end of a sentence. This is different from most other styles, where there is normally a period.

Yes, you should normally include an access date in an AMA website citation (or when citing any source with a URL). This is because webpages can change their content over time, so it’s useful for the reader to know when you accessed the page.

When a publication or update date is provided on the page, you should include it in addition to the access date. The access date appears second in this case, e.g., ‘Published June 19, 2021. Accessed August 29, 2022.’

Don’t include an access date when citing a source with a DOI (such as in an AMA journal article citation ).

Some variables have fixed levels. For example, gender and ethnicity are always nominal level data because they cannot be ranked.

However, for other variables, you can choose the level of measurement . For example, income is a variable that can be recorded on an ordinal or a ratio scale:

• At an ordinal level , you could create 5 income groupings and code the incomes that fall within them from 1–5.
• At a ratio level , you would record exact numbers for income.

If you have a choice, the ratio level is always preferable because you can analyse data in more ways. The higher the level of measurement, the more precise your data is.

The level at which you measure a variable determines how you can analyse your data.

Depending on the level of measurement , you can perform different descriptive statistics to get an overall summary of your data and inferential statistics to see if your results support or refute your hypothesis .

Levels of measurement tell you how precisely variables are recorded. There are 4 levels of measurement, which can be ranked from low to high:

• Nominal : the data can only be categorised.
• Ordinal : the data can be categorised and ranked.
• Interval : the data can be categorised and ranked, and evenly spaced.
• Ratio : the data can be categorised, ranked, evenly spaced and has a natural zero.

Statistical analysis is the main method for analyzing quantitative research data . It uses probabilities and models to test predictions about a population from sample data.

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

As the degrees of freedom increase, Student’s t distribution becomes less leptokurtic , meaning that the probability of extreme values decreases. The distribution becomes more and more similar to a standard normal distribution .

When there are only one or two degrees of freedom , the chi-square distribution is shaped like a backwards ‘J’. When there are three or more degrees of freedom, the distribution is shaped like a right-skewed hump. As the degrees of freedom increase, the hump becomes less right-skewed and the peak of the hump moves to the right. The distribution becomes more and more similar to a normal distribution .

‘Looking forward in hearing from you’ is an incorrect version of the phrase looking forward to hearing from you . The phrasal verb ‘looking forward to’ always needs the preposition ‘to’, not ‘in’.

• I am looking forward in hearing from you.
• I am looking forward to hearing from you.

Some synonyms and near synonyms for the expression looking forward to hearing from you include:

• Eagerly awaiting your response
• Hoping to hear from you soon
• It would be great to hear back from you
• Thanks in advance for your reply

People sometimes mistakenly write ‘looking forward to hear from you’, but this is incorrect. The correct phrase is looking forward to hearing from you .

The phrasal verb ‘look forward to’ is always followed by a direct object, the thing you’re looking forward to. As the direct object has to be a noun phrase , it should be the gerund ‘hearing’, not the verb ‘hear’.

• I’m looking forward to hear from you soon.
• I’m looking forward to hearing from you soon.

Traditionally, the sign-off Yours sincerely is used in an email message or letter when you are writing to someone you have interacted with before, not a complete stranger.

Yours faithfully is used instead when you are writing to someone you have had no previous correspondence with, especially if you greeted them as ‘ Dear Sir or Madam ’.

Just checking in   is a standard phrase used to start an email (or other message) that’s intended to ask someone for a response or follow-up action in a friendly, informal way. However, it’s a cliché opening that can come across as passive-aggressive, so we recommend avoiding it in favor of a more direct opening like “We previously discussed …”

In a more personal context, you might encounter “just checking in” as part of a longer phrase such as “I’m just checking in to see how you’re doing”. In this case, it’s not asking the other person to do anything but rather asking about their well-being (emotional or physical) in a friendly way.

“Earliest convenience” is part of the phrase at your earliest convenience , meaning “as soon as you can”. 

It’s typically used to end an email in a formal context by asking the recipient to do something when it’s convenient for them to do so.

ASAP is an abbreviation of the phrase “as soon as possible”. 

It’s typically used to indicate a sense of urgency in highly informal contexts (e.g., “Let me know ASAP if you need me to drive you to the airport”).

“ASAP” should be avoided in more formal correspondence. Instead, use an alternative like at your earliest convenience .

Some synonyms and near synonyms of the verb   compose   (meaning “to make up”) are:

People increasingly use “comprise” as a synonym of “compose.” However, this is normally still seen as a mistake, and we recommend avoiding it in your academic writing . “Comprise” traditionally means “to be made up of,” not “to make up.”

Some synonyms and near synonyms of the verb comprise are:

• Be composed of
• Be made up of

People increasingly use “comprise” interchangeably with “compose,” meaning that they consider words like “compose,” “constitute,” and “form” to be synonymous with “comprise.” However, this is still normally regarded as an error, and we advise against using these words interchangeably in academic writing .

A fallacy is a mistaken belief, particularly one based on unsound arguments or one that lacks the evidence to support it. Common types of fallacy that may compromise the quality of your research are:

• Correlation/causation fallacy: Claiming that two events that occur together have a cause-and-effect relationship even though this can’t be proven
• Ecological fallacy : Making inferences about the nature of individuals based on aggregate data for the group
• The sunk cost fallacy : Following through on a project or decision because we have already invested time, effort, or money into it, even if the current costs outweigh the benefits
• The base-rate fallacy : Ignoring base-rate or statistically significant information, such as sample size or the relative frequency of an event, in favor of  less relevant information e.g., pertaining to a single case, or a small number of cases
• The planning fallacy : Underestimating the time needed to complete a future task, even when we know that similar tasks in the past have taken longer than planned

The planning fallacy refers to people’s tendency to underestimate the resources needed to complete a future task, despite knowing that previous tasks have also taken longer than planned.

For example, people generally tend to underestimate the cost and time needed for construction projects. The planning fallacy occurs due to people’s tendency to overestimate the chances that positive events, such as a shortened timeline, will happen to them. This phenomenon is called optimism bias or positivity bias.

Although both red herring fallacy and straw man fallacy are logical fallacies or reasoning errors, they denote different attempts to “win” an argument. More specifically:

• A red herring fallacy refers to an attempt to change the subject and divert attention from the original issue. In other words, a seemingly solid but ultimately irrelevant argument is introduced into the discussion, either on purpose or by mistake.
• A straw man argument involves the deliberate distortion of another person’s argument. By oversimplifying or exaggerating it, the other party creates an easy-to-refute argument and then attacks it.

The red herring fallacy is a problem because it is flawed reasoning. It is a distraction device that causes people to become sidetracked from the main issue and draw wrong conclusions.

Although a red herring may have some kernel of truth, it is used as a distraction to keep our eyes on a different matter. As a result, it can cause us to accept and spread misleading information.

The sunk cost fallacy and escalation of commitment (or commitment bias ) are two closely related terms. However, there is a slight difference between them:

• Escalation of commitment (aka commitment bias ) is the tendency to be consistent with what we have already done or said we will do in the past, especially if we did so in public. In other words, it is an attempt to save face and appear consistent.
• Sunk cost fallacy is the tendency to stick with a decision or a plan even when it’s failing. Because we have already invested valuable time, money, or energy, quitting feels like these resources were wasted.

In other words, escalating commitment is a manifestation of the sunk cost fallacy: an irrational escalation of commitment frequently occurs when people refuse to accept that the resources they’ve already invested cannot be recovered. Instead, they insist on more spending to justify the initial investment (and the incurred losses).

When you are faced with a straw man argument , the best way to respond is to draw attention to the fallacy and ask your discussion partner to show how your original statement and their distorted version are the same. Since these are different, your partner will either have to admit that their argument is invalid or try to justify it by using more flawed reasoning, which you can then attack.

The straw man argument is a problem because it occurs when we fail to take an opposing point of view seriously. Instead, we intentionally misrepresent our opponent’s ideas and avoid genuinely engaging with them. Due to this, resorting to straw man fallacy lowers the standard of constructive debate.

A straw man argument is a distorted (and weaker) version of another person’s argument that can easily be refuted (e.g., when a teacher proposes that the class spend more time on math exercises, a parent complains that the teacher doesn’t care about reading and writing).

This is a straw man argument because it misrepresents the teacher’s position, which didn’t mention anything about cutting down on reading and writing. The straw man argument is also known as the straw man fallacy .

A slippery slope argument is not always a fallacy.

• When someone claims adopting a certain policy or taking a certain action will automatically lead to a series of other policies or actions also being taken, this is a slippery slope argument.
• If they don’t show a causal connection between the advocated policy and the consequent policies, then they commit a slippery slope fallacy .

There are a number of ways you can deal with slippery slope arguments especially when you suspect these are fallacious:

• Slippery slope arguments take advantage of the gray area between an initial action or decision and the possible next steps that might lead to the undesirable outcome. You can point out these missing steps and ask your partner to indicate what evidence exists to support the claimed relationship between two or more events.
• Ask yourself if each link in the chain of events or action is valid. Every proposition has to be true for the overall argument to work, so even if one link is irrational or not supported by evidence, then the argument collapses.
• Sometimes people commit a slippery slope fallacy unintentionally. In these instances, use an example that demonstrates the problem with slippery slope arguments in general (e.g., by using statements to reach a conclusion that is not necessarily relevant to the initial statement). By attacking the concept of slippery slope arguments you can show that they are often fallacious.

People sometimes confuse cognitive bias and logical fallacies because they both relate to flawed thinking. However, they are not the same:

• Cognitive bias is the tendency to make decisions or take action in an illogical way because of our values, memory, socialization, and other personal attributes. In other words, it refers to a fixed pattern of thinking rooted in the way our brain works.
• Logical fallacies relate to how we make claims and construct our arguments in the moment. They are statements that sound convincing at first but can be disproven through logical reasoning.

In other words, cognitive bias refers to an ongoing predisposition, while logical fallacy refers to mistakes of reasoning that occur in the moment.

An appeal to ignorance (ignorance here meaning lack of evidence) is a type of informal logical fallacy .

It asserts that something must be true because it hasn’t been proven false—or that something must be false because it has not yet been proven true.

For example, “unicorns exist because there is no evidence that they don’t.” The appeal to ignorance is also called the burden of proof fallacy .

An ad hominem (Latin for “to the person”) is a type of informal logical fallacy . Instead of arguing against a person’s position, an ad hominem argument attacks the person’s character or actions in an effort to discredit them.

This rhetorical strategy is fallacious because a person’s character, motive, education, or other personal trait is logically irrelevant to whether their argument is true or false.

Name-calling is common in ad hominem fallacy (e.g., “environmental activists are ineffective because they’re all lazy tree-huggers”).

Ad hominem is a persuasive technique where someone tries to undermine the opponent’s argument by personally attacking them.

In this way, one can redirect the discussion away from the main topic and to the opponent’s personality without engaging with their viewpoint. When the opponent’s personality is irrelevant to the discussion, we call it an ad hominem fallacy .

Ad hominem tu quoque (‘you too”) is an attempt to rebut a claim by attacking its proponent on the grounds that they uphold a double standard or that they don’t practice what they preach. For example, someone is telling you that you should drive slowly otherwise you’ll get a speeding ticket one of these days, and you reply “but you used to get them all the time!”

Argumentum ad hominem means “argument to the person” in Latin and it is commonly referred to as ad hominem argument or personal attack. Ad hominem arguments are used in debates to refute an argument by attacking the character of the person making it, instead of the logic or premise of the argument itself.

The opposite of the hasty generalization fallacy is called slothful induction fallacy or appeal to coincidence .

It is the tendency to deny a conclusion even though there is sufficient evidence that supports it. Slothful induction occurs due to our natural tendency to dismiss events or facts that do not align with our personal biases and expectations. For example, a researcher may try to explain away unexpected results by claiming it is just a coincidence.

To avoid a hasty generalization fallacy we need to ensure that the conclusions drawn are well-supported by the appropriate evidence. More specifically:

• In statistics , if we want to draw inferences about an entire population, we need to make sure that the sample is random and representative of the population . We can achieve that by using a probability sampling method , like simple random sampling or stratified sampling .
• In academic writing , use precise language and measured phases. Try to avoid making absolute claims, cite specific instances and examples without applying the findings to a larger group.
• As readers, we need to ask ourselves “does the writer demonstrate sufficient knowledge of the situation or phenomenon that would allow them to make a generalization?”

The hasty generalization fallacy and the anecdotal evidence fallacy are similar in that they both result in conclusions drawn from insufficient evidence. However, there is a difference between the two:

• The hasty generalization fallacy involves genuinely considering an example or case (i.e., the evidence comes first and then an incorrect conclusion is drawn from this).
• The anecdotal evidence fallacy (also known as “cherry-picking” ) is knowing in advance what conclusion we want to support, and then selecting the story (or a few stories) that support it. By overemphasizing anecdotal evidence that fits well with the point we are trying to make, we overlook evidence that would undermine our argument.

Although many sources use circular reasoning fallacy and begging the question interchangeably, others point out that there is a subtle difference between the two:

• Begging the question fallacy occurs when you assume that an argument is true in order to justify a conclusion. If something begs the question, what you are actually asking is, “Is the premise of that argument actually true?” For example, the statement “Snakes make great pets. That’s why we should get a snake” begs the question “are snakes really great pets?”
• Circular reasoning fallacy on the other hand, occurs when the evidence used to support a claim is just a repetition of the claim itself.  For example, “People have free will because they can choose what to do.”

In other words, we could say begging the question is a form of circular reasoning.

Circular reasoning fallacy uses circular reasoning to support an argument. More specifically, the evidence used to support a claim is just a repetition of the claim itself. For example: “The President of the United States is a good leader (claim), because they are the leader of this country (supporting evidence)”.

An example of a non sequitur is the following statement:

“Giving up nuclear weapons weakened the United States’ military. Giving up nuclear weapons also weakened China. For this reason, it is wrong to try to outlaw firearms in the United States today.”

Clearly there is a step missing in this line of reasoning and the conclusion does not follow from the premise, resulting in a non sequitur fallacy .

The difference between the post hoc fallacy and the non sequitur fallacy is that post hoc fallacy infers a causal connection between two events where none exists, whereas the non sequitur fallacy infers a conclusion that lacks a logical connection to the premise.

In other words, a post hoc fallacy occurs when there is a lack of a cause-and-effect relationship, while a non sequitur fallacy occurs when there is a lack of logical connection.

An example of post hoc fallacy is the following line of reasoning:

“Yesterday I had ice cream, and today I have a terrible stomachache. I’m sure the ice cream caused this.”

Although it is possible that the ice cream had something to do with the stomachache, there is no proof to justify the conclusion other than the order of events. Therefore, this line of reasoning is fallacious.

Post hoc fallacy and hasty generalisation fallacy are similar in that they both involve jumping to conclusions. However, there is a difference between the two:

• Post hoc fallacy is assuming a cause and effect relationship between two events, simply because one happened after the other.
• Hasty generalisation fallacy is drawing a general conclusion from a small sample or little evidence.

In other words, post hoc fallacy involves a leap to a causal claim; hasty generalisation fallacy involves a leap to a general proposition.

The fallacy of composition is similar to and can be confused with the hasty generalization fallacy . However, there is a difference between the two:

• The fallacy of composition involves drawing an inference about the characteristics of a whole or group based on the characteristics of its individual members.
• The hasty generalization fallacy involves drawing an inference about a population or class of things on the basis of few atypical instances or a small sample of that population or thing.

In other words, the fallacy of composition is using an unwarranted assumption that we can infer something about a whole based on the characteristics of its parts, while the hasty generalization fallacy is using insufficient evidence to draw a conclusion.

The opposite of the fallacy of composition is the fallacy of division . In the fallacy of division, the assumption is that a characteristic which applies to a whole or a group must necessarily apply to the parts or individual members. For example, “Australians travel a lot. Gary is Australian, so he must travel a lot.”

Base rate fallacy can be avoided by following these steps:

• Avoid making an important decision in haste. When we are under pressure, we are more likely to resort to cognitive shortcuts like the availability heuristic and the representativeness heuristic . Due to this, we are more likely to factor in only current and vivid information, and ignore the actual probability of something happening (i.e., base rate).
• Take a long-term view on the decision or question at hand. Look for relevant statistical data, which can reveal long-term trends and give you the full picture.
• Talk to experts like professionals. They are more aware of probabilities related to specific decisions.

Suppose there is a population consisting of 90% psychologists and 10% engineers. Given that you know someone enjoyed physics at school, you may conclude that they are an engineer rather than a psychologist, even though you know that this person comes from a population consisting of far more psychologists than engineers.

When we ignore the rate of occurrence of some trait in a population (the base-rate information) we commit base rate fallacy .

Cost-benefit fallacy is a common error that occurs when allocating sources in project management. It is the fallacy of assuming that cost-benefit estimates are more or less accurate, when in fact they are highly inaccurate and biased. This means that cost-benefit analyses can be useful, but only after the cost-benefit fallacy has been acknowledged and corrected for. Cost-benefit fallacy is a type of base rate fallacy .

In advertising, the fallacy of equivocation is often used to create a pun. For example, a billboard company might advertise their billboards using a line like: “Looking for a sign? This is it!” The word sign has a literal meaning as billboard and a figurative one as a sign from God, the universe, etc.

Equivocation is a fallacy because it is a form of argumentation that is both misleading and logically unsound. When the meaning of a word or phrase shifts in the course of an argument, it causes confusion and also implies that the conclusion (which may be true) does not follow from the premise.

The fallacy of equivocation is an informal logical fallacy, meaning that the error lies in the content of the argument instead of the structure.

Fallacies of relevance are a group of fallacies that occur in arguments when the premises are logically irrelevant to the conclusion. Although at first there seems to be a connection between the premise and the conclusion, in reality fallacies of relevance use unrelated forms of appeal.

For example, the genetic fallacy makes an appeal to the source or origin of the claim in an attempt to assert or refute something.

The ad hominem fallacy and the genetic fallacy are closely related in that they are both fallacies of relevance. In other words, they both involve arguments that use evidence or examples that are not logically related to the argument at hand. However, there is a difference between the two:

• In the ad hominem fallacy , the goal is to discredit the argument by discrediting the person currently making the argument.
• In the genetic fallacy , the goal is to discredit the argument by discrediting the history or origin (i.e., genesis) of an argument.

False dilemma fallacy is also known as false dichotomy, false binary, and “either-or” fallacy. It is the fallacy of presenting only two choices, outcomes, or sides to an argument as the only possibilities, when more are available.

The false dilemma fallacy works in two ways:

• By presenting only two options as if these were the only ones available
• By presenting two options as mutually exclusive (i.e., only one option can be selected or can be true at a time)

In both cases, by using the false dilemma fallacy, one conceals alternative choices and doesn’t allow others to consider the full range of options. This is usually achieved through an“either-or” construction and polarised, divisive language (“you are either a friend or an enemy”).

The best way to avoid a false dilemma fallacy is to pause and reflect on two points:

• Are the options presented truly the only ones available ? It could be that another option has been deliberately omitted.
• Are the options mentioned mutually exclusive ? Perhaps all of the available options can be selected (or be true) at the same time, which shows that they aren’t mutually exclusive. Proving this is called “escaping between the horns of the dilemma.”

Begging the question fallacy is an argument in which you assume what you are trying to prove. In other words, your position and the justification of that position are the same, only slightly rephrased.

For example: “All freshmen should attend college orientation, because all college students should go to such an orientation.”

The complex question fallacy and begging the question fallacy are similar in that they are both based on assumptions. However, there is a difference between them:

• A complex question fallacy occurs when someone asks a question that presupposes the answer to another question that has not been established or accepted by the other person. For example, asking someone “Have you stopped cheating on tests?”, unless it has previously been established that the person is indeed cheating on tests, is a fallacy.
• Begging the question fallacy occurs when we assume the very thing as a premise that we’re trying to prove in our conclusion. In other words, the conclusion is used to support the premises, and the premises prove the validity of the conclusion. For example: “God exists because the Bible says so, and the Bible is true because it is the word of God.”

In other words, begging the question is about drawing a conclusion based on an assumption, while a complex question involves asking a question that presupposes the answer to a prior question.

“ No true Scotsman ” arguments aren’t always fallacious. When there is a generally accepted definition of who or what constitutes a group, it’s reasonable to use statements in the form of “no true Scotsman”.

For example, the statement that “no true pacifist would volunteer for military service” is not fallacious, since a pacifist is, by definition, someone who opposes war or violence as a means of settling disputes.

No true Scotsman arguments are fallacious because instead of logically refuting the counterexample, they simply assert that it doesn’t count. In other words, the counterexample is rejected for psychological, but not logical, reasons.

The appeal to purity or no true Scotsman fallacy is an attempt to defend a generalisation about a group from a counterexample by shifting the definition of the group in the middle of the argument. In this way, one can exclude the counterexample as not being “true”, “genuine”, or “pure” enough to be considered as part of the group in question.

To identify an appeal to authority fallacy , you can ask yourself the following questions:

• Is the authority cited really a qualified expert in this particular area under discussion? For example, someone who has formal education or years of experience can be an expert.
• Do experts disagree on this particular subject? If that is the case, then for almost any claim supported by one expert there will be a counterclaim that is supported by another expert. If there is no consensus, an appeal to authority is fallacious.
• Is the authority in question biased? If you suspect that an expert’s prejudice and bias could have influenced their views, then the expert is not reliable and an argument citing this expert will be fallacious.To identify an appeal to authority fallacy, you ask yourself whether the authority cited is a qualified expert in the particular area under discussion.

Appeal to authority is a fallacy when those who use it do not provide any justification to support their argument. Instead they cite someone famous who agrees with their viewpoint, but is not qualified to make reliable claims on the subject.

Appeal to authority fallacy is often convincing because of the effect authority figures have on us. When someone cites a famous person, a well-known scientist, a politician, etc. people tend to be distracted and often fail to critically examine whether the authority figure is indeed an expert in the area under discussion.

The ad populum fallacy is common in politics. One example is the following viewpoint: “The majority of our countrymen think we should have military operations overseas; therefore, it’s the right thing to do.”

This line of reasoning is fallacious, because popular acceptance of a belief or position does not amount to a justification of that belief. In other words, following the prevailing opinion without examining the underlying reasons is irrational.

The ad populum fallacy plays on our innate desire to fit in (known as “bandwagon effect”). If many people believe something, our common sense tells us that it must be true and we tend to accept it. However, in logic, the popularity of a proposition cannot serve as evidence of its truthfulness.

Ad populum (or appeal to popularity) fallacy and appeal to authority fallacy are similar in that they both conflate the validity of a belief with its popular acceptance among a specific group. However there is a key difference between the two:

• An ad populum fallacy tries to persuade others by claiming that something is true or right because a lot of people think so.
• An appeal to authority fallacy tries to persuade by claiming a group of experts believe something is true or right, therefore it must be so.

To identify a false cause fallacy , you need to carefully analyse the argument:

• When someone claims that one event directly causes another, ask if there is sufficient evidence to establish a cause-and-effect relationship. 
• Ask if the claim is based merely on the chronological order or co-occurrence of the two events. 
• Consider alternative possible explanations (are there other factors at play that could influence the outcome?).

By carefully analysing the reasoning, considering alternative explanations, and examining the evidence provided, you can identify a false cause fallacy and discern whether a causal claim is valid or flawed.

False cause fallacy examples include: 

• Believing that wearing your lucky jersey will help your team win 
• Thinking that everytime you wash your car, it rains
• Claiming that playing video games causes violent behavior 

In each of these examples, we falsely assume that one event causes another without any proof.

The planning fallacy and procrastination are not the same thing. Although they both relate to time and task management, they describe different challenges:

• The planning fallacy describes our inability to correctly estimate how long a future task will take, mainly due to optimism bias and a strong focus on the best-case scenario.
• Procrastination refers to postponing a task, usually by focusing on less urgent or more enjoyable activities. This is due to psychological reasons, like fear of failure.

In other words, the planning fallacy refers to inaccurate predictions about the time we need to finish a task, while procrastination is a deliberate delay due to psychological factors.

A real-life example of the planning fallacy is the construction of the Sydney Opera House in Australia. When construction began in the late 1950s, it was initially estimated that it would be completed in four years at a cost of around $7 million.

Because the government wanted the construction to start before political opposition would stop it and while public opinion was still favorable, a number of design issues had not been carefully studied in advance. Due to this, several problems appeared immediately after the project commenced.

The construction process eventually stretched over 14 years, with the Opera House being completed in 1973 at a cost of over $100 million, significantly exceeding the initial estimates.

An example of appeal to pity fallacy is the following appeal by a student to their professor:

“Professor, please consider raising my grade. I had a terrible semester: my car broke down, my laptop got stolen, and my cat got sick.”

While these circumstances may be unfortunate, they are not directly related to the student’s academic performance.

While both the appeal to pity fallacy and   red herring fallacy can serve as a distraction from the original discussion topic, they are distinct fallacies. More specifically:

• Appeal to pity fallacy attempts to evoke feelings of sympathy, pity, or guilt in an audience, so that they accept the speaker’s conclusion as truthful.
• Red herring fallacy attempts to introduce an irrelevant piece of information that diverts the audience’s attention to a different topic.

Both fallacies can be used as a tool of deception. However, they operate differently and serve distinct purposes in arguments.

Argumentum ad misericordiam (Latin for “argument from pity or misery”) is another name for appeal to pity fallacy . It occurs when someone evokes sympathy or guilt in an attempt to gain support for their claim, without providing any logical reasons to support the claim itself. Appeal to pity is a deceptive tactic of argumentation, playing on people’s emotions to sway their opinion.

Yes, it’s quite common to start a sentence with a preposition, and there’s no reason not to do so.

For example, the sentence “ To many, she was a hero” is perfectly grammatical. It could also be rephrased as “She was a hero to  many”, but there’s no particular reason to do so. Both versions are fine.

Some people argue that you shouldn’t end a sentence with a preposition , but that “rule” can also be ignored, since it’s not supported by serious language authorities.

Yes, it’s fine to end a sentence with a preposition . The “rule” against doing so is overwhelmingly rejected by modern style guides and language authorities and is based on the rules of Latin grammar, not English.

Trying to avoid ending a sentence with a preposition often results in very unnatural phrasings. For example, turning “He knows what he’s talking about ” into “He knows about what he’s talking” or “He knows that about which he’s talking” is definitely not an improvement.

No, ChatGPT is not a credible source of factual information and can’t be cited for this purpose in academic writing . While it tries to provide accurate answers, it often gets things wrong because its responses are based on patterns, not facts and data.

Specifically, the CRAAP test for evaluating sources includes five criteria: currency , relevance , authority , accuracy , and purpose . ChatGPT fails to meet at least three of them:

• Currency: The dataset that ChatGPT was trained on only extends to 2021, making it slightly outdated.
• Authority: It’s just a language model and is not considered a trustworthy source of factual information.
• Accuracy: It bases its responses on patterns rather than evidence and is unable to cite its sources .

So you shouldn’t cite ChatGPT as a trustworthy source for a factual claim. You might still cite ChatGPT for other reasons – for example, if you’re writing a paper about AI language models, ChatGPT responses are a relevant primary source .

ChatGPT is an AI language model that was trained on a large body of text from a variety of sources (e.g., Wikipedia, books, news articles, scientific journals). The dataset only went up to 2021, meaning that it lacks information on more recent events.

It’s also important to understand that ChatGPT doesn’t access a database of facts to answer your questions. Instead, its responses are based on patterns that it saw in the training data.

So ChatGPT is not always trustworthy . It can usually answer general knowledge questions accurately, but it can easily give misleading answers on more specialist topics.

Another consequence of this way of generating responses is that ChatGPT usually can’t cite its sources accurately. It doesn’t really know what source it’s basing any specific claim on. It’s best to check any information you get from it against a credible source .

No, it is not possible to cite your sources with ChatGPT . You can ask it to create citations, but it isn’t designed for this task and tends to make up sources that don’t exist or present information in the wrong format. ChatGPT also cannot add citations to direct quotes in your text.

Instead, use a tool designed for this purpose, like the Scribbr Citation Generator .

But you can use ChatGPT for assignments in other ways, to provide inspiration, feedback, and general writing advice.

GPT  stands for “generative pre-trained transformer”, which is a type of large language model: a neural network trained on a very large amount of text to produce convincing, human-like language outputs. The Chat part of the name just means “chat”: ChatGPT is a chatbot that you interact with by typing in text.

The technology behind ChatGPT is GPT-3.5 (in the free version) or GPT-4 (in the premium version). These are the names for the specific versions of the GPT model. GPT-4 is currently the most advanced model that OpenAI has created. It’s also the model used in Bing’s chatbot feature.

ChatGPT was created by OpenAI, an AI research company. It started as a nonprofit company in 2015 but became for-profit in 2019. Its CEO is Sam Altman, who also co-founded the company. OpenAI released ChatGPT as a free “research preview” in November 2022. Currently, it’s still available for free, although a more advanced premium version is available if you pay for it.

OpenAI is also known for developing DALL-E, an AI image generator that runs on similar technology to ChatGPT.

ChatGPT is owned by OpenAI, the company that developed and released it. OpenAI is a company dedicated to AI research. It started as a nonprofit company in 2015 but transitioned to for-profit in 2019. Its current CEO is Sam Altman, who also co-founded the company.

In terms of who owns the content generated by ChatGPT, OpenAI states that it will not claim copyright on this content , and the terms of use state that “you can use Content for any purpose, including commercial purposes such as sale or publication”. This means that you effectively own any content you generate with ChatGPT and can use it for your own purposes.

Be cautious about how you use ChatGPT content in an academic context. University policies on AI writing are still developing, so even if you “own” the content, you’re often not allowed to submit it as your own work according to your university or to publish it in a journal.

ChatGPT is a chatbot based on a large language model (LLM). These models are trained on huge datasets consisting of hundreds of billions of words of text, based on which the model learns to effectively predict natural responses to the prompts you enter.

ChatGPT was also refined through a process called reinforcement learning from human feedback (RLHF), which involves “rewarding” the model for providing useful answers and discouraging inappropriate answers – encouraging it to make fewer mistakes.

Essentially, ChatGPT’s answers are based on predicting the most likely responses to your inputs based on its training data, with a reward system on top of this to incentivise it to give you the most helpful answers possible. It’s a bit like an incredibly advanced version of predictive text. This is also one of ChatGPT’s limitations : because its answers are based on probabilities, they’re not always trustworthy .

OpenAI may store ChatGPT conversations for the purposes of future training. Additionally, these conversations may be monitored by human AI trainers.

Users can choose not to have their chat history saved. Unsaved chats are not used to train future models and are permanently deleted from ChatGPT’s system after 30 days.

The official ChatGPT app is currently only available on iOS devices. If you don’t have an iOS device, only use the official OpenAI website to access the tool. This helps to eliminate the potential risk of downloading fraudulent or malicious software.

ChatGPT conversations are generally used to train future models and to resolve issues/bugs. These chats may be monitored by human AI trainers.

However, users can opt out of having their conversations used for training. In these instances, chats are monitored only for potential abuse.

Yes, using ChatGPT as a conversation partner is a great way to practice a language in an interactive way.

Try using a prompt like this one:

“Please be my Spanish conversation partner. Only speak to me in Spanish. Keep your answers short (maximum 50 words). Ask me questions. Let’s start the conversation with the following topic: [conversation topic].”

Yes, there are a variety of ways to use ChatGPT for language learning , including treating it as a conversation partner, asking it for translations, and using it to generate a curriculum or practice exercises.

AI detectors aim to identify the presence of AI-generated text (e.g., from ChatGPT ) in a piece of writing, but they can’t do so with complete accuracy. In our comparison of the best AI detectors , we found that the 10 tools we tested had an average accuracy of 60%. The best free tool had 68% accuracy, the best premium tool 84%.

Because of how AI detectors work , they can never guarantee 100% accuracy, and there is always at least a small risk of false positives (human text being marked as AI-generated). Therefore, these tools should not be relied upon to provide absolute proof that a text is or isn’t AI-generated. Rather, they can provide a good indication in combination with other evidence.

Tools called AI detectors are designed to label text as AI-generated or human. AI detectors work by looking for specific characteristics in the text, such as a low level of randomness in word choice and sentence length. These characteristics are typical of AI writing, allowing the detector to make a good guess at when text is AI-generated.

But these tools can’t guarantee 100% accuracy. Check out our comparison of the best AI detectors to learn more.

You can also manually watch for clues that a text is AI-generated – for example, a very different style from the writer’s usual voice or a generic, overly polite tone.

Our research into the best summary generators (aka summarisers or summarising tools) found that the best summariser available in 2023 is the one offered by QuillBot.

While many summarisers just pick out some sentences from the text, QuillBot generates original summaries that are creative, clear, accurate, and concise. It can summarise texts of up to 1,200 words for free, or up to 6,000 with a premium subscription.

Try the QuillBot summarizer for free

Deep learning requires a large dataset (e.g., images or text) to learn from. The more diverse and representative the data, the better the model will learn to recognise objects or make predictions. Only when the training data is sufficiently varied can the model make accurate predictions or recognise objects from new data.

Deep learning models can be biased in their predictions if the training data consist of biased information. For example, if a deep learning model used for screening job applicants has been trained with a dataset consisting primarily of white male applicants, it will consistently favour this specific population over others.

A good ChatGPT prompt (i.e., one that will get you the kinds of responses you want):

• Gives the tool a role to explain what type of answer you expect from it
• Is precisely formulated and gives enough context
• Is free from bias
• Has been tested and improved by experimenting with the tool

ChatGPT prompts are the textual inputs (e.g., questions, instructions) that you enter into ChatGPT to get responses.

ChatGPT predicts an appropriate response to the prompt you entered. In general, a more specific and carefully worded prompt will get you better responses.

Yes, ChatGPT is currently available for free. You have to sign up for a free account to use the tool, and you should be aware that your data may be collected to train future versions of the model.

To sign up and use the tool for free, go to this page and click “Sign up”. You can do so with your email or with a Google account.

A premium version of the tool called ChatGPT Plus is available as a monthly subscription. It currently costs £16 and gets you access to features like GPT-4 (a more advanced version of the language model). But it’s optional: you can use the tool completely free if you’re not interested in the extra features.

You can access ChatGPT by signing up for a free account:

• Follow this link to the ChatGPT website.
• Click on “Sign up” and fill in the necessary details (or use your Google account). It’s free to sign up and use the tool.
• Type a prompt into the chat box to get started!

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The tool is unlikely to be consistently biased toward a particular perspective or against a particular group. Rather, its responses are based on its training data and on the way you phrase your ChatGPT prompts . It’s sensitive to phrasing, so asking it the same question in different ways will result in quite different answers.

Information extraction  refers to the process of starting from unstructured sources (e.g., text documents written in ordinary English) and automatically extracting structured information (i.e., data in a clearly defined format that’s easily understood by computers). It’s an important concept in natural language processing (NLP) .

For example, you might think of using news articles full of celebrity gossip to automatically create a database of the relationships between the celebrities mentioned (e.g., married, dating, divorced, feuding). You would end up with data in a structured format, something like MarriageBetween(celebrity 1 ,celebrity 2 ,date) .

The challenge involves developing systems that can “understand” the text well enough to extract this kind of data from it.

Knowledge representation and reasoning (KRR) is the study of how to represent information about the world in a form that can be used by a computer system to solve and reason about complex problems. It is an important field of artificial intelligence (AI) research.

An example of a KRR application is a semantic network, a way of grouping words or concepts by how closely related they are and formally defining the relationships between them so that a machine can “understand” language in something like the way people do.

A related concept is information extraction , concerned with how to get structured information from unstructured sources.

Yes, you can use ChatGPT to summarise text . This can help you understand complex information more easily, summarise the central argument of your own paper, or clarify your research question.

You can also use Scribbr’s free text summariser , which is designed specifically for this purpose.

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However, it’s not specifically designed for this purpose. We recommend using a specialised tool like Scribbr’s free paraphrasing tool , which will provide a smoother user experience.

Yes, you use ChatGPT to help write your college essay by having it generate feedback on certain aspects of your work (consistency of tone, clarity of structure, etc.).

However, ChatGPT is not able to adequately judge qualities like vulnerability and authenticity. For this reason, it’s important to also ask for feedback from people who have experience with college essays and who know you well. Alternatively, you can get advice using Scribbr’s essay editing service .

No, having ChatGPT write your college essay can negatively impact your application in numerous ways. ChatGPT outputs are unoriginal and lack personal insight.

Furthermore, Passing off AI-generated text as your own work is considered academically dishonest . AI detectors may be used to detect this offense, and it’s highly unlikely that any university will accept you if you are caught submitting an AI-generated admission essay.

However, you can use ChatGPT to help write your college essay during the preparation and revision stages (e.g., for brainstorming ideas and generating feedback).

ChatGPT and other AI writing tools can have unethical uses. These include:

• Reproducing biases and false information
• Using ChatGPT to cheat in academic contexts
• Violating the privacy of others by inputting personal information

However, when used correctly, AI writing tools can be helpful resources for improving your academic writing and research skills. Some ways to use ChatGPT ethically include:

• Following your institution’s guidelines
• Critically evaluating outputs
• Being transparent about how you used the tool

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This is the Difference Between a Hypothesis and a Theory

What to Know A hypothesis is an assumption made before any research has been done. It is formed so that it can be tested to see if it might be true. A theory is a principle formed to explain the things already shown in data. Because of the rigors of experiment and control, it is much more likely that a theory will be true than a hypothesis.

As anyone who has worked in a laboratory or out in the field can tell you, science is about process: that of observing, making inferences about those observations, and then performing tests to see if the truth value of those inferences holds up. The scientific method is designed to be a rigorous procedure for acquiring knowledge about the world around us.

In scientific reasoning, a hypothesis is constructed before any applicable research has been done. A theory, on the other hand, is supported by evidence: it's a principle formed as an attempt to explain things that have already been substantiated by data.

Toward that end, science employs a particular vocabulary for describing how ideas are proposed, tested, and supported or disproven. And that's where we see the difference between a hypothesis and a theory .

A hypothesis is an assumption, something proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

What is a Hypothesis?

A hypothesis is usually tentative, an assumption or suggestion made strictly for the objective of being tested.

When a character which has been lost in a breed, reappears after a great number of generations, the most probable hypothesis is, not that the offspring suddenly takes after an ancestor some hundred generations distant, but that in each successive generation there has been a tendency to reproduce the character in question, which at last, under unknown favourable conditions, gains an ascendancy. Charles Darwin, On the Origin of Species , 1859 According to one widely reported hypothesis , cell-phone transmissions were disrupting the bees' navigational abilities. (Few experts took the cell-phone conjecture seriously; as one scientist said to me, "If that were the case, Dave Hackenberg's hives would have been dead a long time ago.") Elizabeth Kolbert, The New Yorker , 6 Aug. 2007

What is a Theory?

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, its likelihood as truth is much higher than that of a hypothesis.

It is evident, on our theory , that coasts merely fringed by reefs cannot have subsided to any perceptible amount; and therefore they must, since the growth of their corals, either have remained stationary or have been upheaved. Now, it is remarkable how generally it can be shown, by the presence of upraised organic remains, that the fringed islands have been elevated: and so far, this is indirect evidence in favour of our theory . Charles Darwin, The Voyage of the Beagle , 1839 An example of a fundamental principle in physics, first proposed by Galileo in 1632 and extended by Einstein in 1905, is the following: All observers traveling at constant velocity relative to one another, should witness identical laws of nature. From this principle, Einstein derived his theory of special relativity. Alan Lightman, Harper's , December 2011

Non-Scientific Use

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch (though theory is more common in this regard):

The theory of the teacher with all these immigrant kids was that if you spoke English loudly enough they would eventually understand. E. L. Doctorow, Loon Lake , 1979 Chicago is famous for asking questions for which there can be no boilerplate answers. Example: given the probability that the federal tax code, nondairy creamer, Dennis Rodman and the art of mime all came from outer space, name something else that has extraterrestrial origins and defend your hypothesis . John McCormick, Newsweek , 5 Apr. 1999 In his mind's eye, Miller saw his case suddenly taking form: Richard Bailey had Helen Brach killed because she was threatening to sue him over the horses she had purchased. It was, he realized, only a theory , but it was one he felt certain he could, in time, prove. Full of urgency, a man with a mission now that he had a hypothesis to guide him, he issued new orders to his troops: Find out everything you can about Richard Bailey and his crowd. Howard Blum, Vanity Fair , January 1995

And sometimes one term is used as a genus, or a means for defining the other:

Laplace's popular version of his astronomy, the Système du monde , was famous for introducing what came to be known as the nebular hypothesis , the theory that the solar system was formed by the condensation, through gradual cooling, of the gaseous atmosphere (the nebulae) surrounding the sun. Louis Menand, The Metaphysical Club , 2001 Researchers use this information to support the gateway drug theory — the hypothesis that using one intoxicating substance leads to future use of another. Jordy Byrd, The Pacific Northwest Inlander , 6 May 2015 Fox, the business and economics columnist for Time magazine, tells the story of the professors who enabled those abuses under the banner of the financial theory known as the efficient market hypothesis . Paul Krugman, The New York Times Book Review , 9 Aug. 2009

Incorrect Interpretations of "Theory"

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general use to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

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Transcript: Ezra Klein Interviews Jennifer Sciubba

Every Tuesday and Friday, Ezra Klein invites you into a conversation about something that matters, like today’s episode with Jennifer Sciubba. Listen wherever you get your podcasts .

Transcripts of our episodes are made available as soon as possible. They are not fully edited for grammar or spelling.

Birthrates Are Plummeting Worldwide. Why?

The demographer jennifer d. sciubba examines why families — even in wealthy nations — are having fewer children..

From New York Times Opinion, this is “The Ezra Klein Show.”

So, for a long time, the population concern we’ve been used to hearing is that we are racing towards too many people too quickly. This was a Malthusian fear in the 18th century that more people would mean more starvation. This was and is the fear of many environmentalists today, that more people means more weight on the planet’s resources, more environmental damage.

But now there’s this other concern that has come to join it, that we are racing towards de-population — too few people too quickly. As countries get richer the world over, fertility rates plummet, plummet quickly. In countries like America, we’re now below replacement rate, the rate at which a population holds steady. You see that in China. You see that in India. In some countries like Japan and South Korea, they’re so far below replacement rate that their population is going to rapidly shrink generation by generation.

If you spend much time on today’s right or among the Silicon Valley VC class, you find the set of fears has become, for them, almost what the climate crisis is for the left. You hear about it constantly. For many, it feels apocalyptic. It is the overarching context in which everything else is playing out.

But even if you don’t quite know how to feel about it, and I don’t always know how to feel about it, it’s also just kind of strange. You wouldn’t necessarily think that societies would have so many fewer children as they become richer. Money makes life easier. Lower child mortality makes the heart rending grief of losing a child less likely. Being better able to provide for your children would maybe make it easier to have more of them. Many people believe a boisterous family is part of the vision of a full life.

But fertility rates, they keep falling and falling. And even in the places where that fall has turned into freefall, where the very fabric of the society is now in question, policy to turn it around is proving completely ineffectual. So, why? We’re going to do two episodes on this, but the first is going to be about the global big picture.

Jennifer Sciubba is a political scientist, a demographer, and the author of the book, “8 Billion and Counting.” I asked her on the show to guide me through what these population numbers actually tell us, what they say in different regions of the world, how they might play out, and what they reveal about what happens to societies as they get richer. As always, my email, [email protected].

Jennifer Sciubba, welcome to the show.

Thank you so much.

So, tell me what the total fertility rate is.

So, the total fertility rate is — let’s just say it’s the average number of children born per woman in her lifetime. It’s a great measure because in one number, you can kind of get a snapshot to compare across time and across places.

So, when I listen to the conversation about total fertility rates, there are sort of two conversations right now happening at the same time. One conversation that I sometimes hear on my left — I get a lot of it in my email inbox for this show — is that it’s way too high. There are too many people. There are going to be even more people.

Through that conversation — I hear it more on my right — it’s all over opinion sections now — is that it’s way too low. We’re facing a demographic bust. We’re going to see population collapse. We are a planet growing old, certainly a bunch of countries growing old. How would you describe the shape of the total fertility rate and how it differs in different places right now?

Well, you nailed it. That’s exactly what the conversation looks like. It’s like Goldilocks is in the room here with us, right? It’s either too high or it’s too low, and it’s never just right. So while we are perceiving this on the left and right in the world today, I will say that it’s, as a student of population history, it’s kind of been like that for a long time, this perception about global fertility.

So, if we look at global population last century, we saw exponential growth, from 1.6 billion at the start of that century to 6.1 billion by the end. Women have, on average, worldwide, about 2.2 children these days. Basically, that is replacement level because that point number, the 0.2 in this case, accounts for children who might not make it to reproductive age. So it’s, in very crude terms, a margin of error. So we’re basically globally at this number.

But in this century so far, we are in a global demographic divide. This is a century about differential growth at this moment. So, we have very low fertility rates in some places, while it’s still high in others. For example, the area in the world where it really is the highest is in parts of sub-Saharan Africa, a handful of countries where it’s still pretty high, over five children per woman on average.

So, there’s a divide, but we’re all moving kind of in the same direction. So when we think about going forward into the second part of the century, that’s really where we’re all going to start converging down at those lower levels. But I could tell you that, and a listener will have a reaction in one direction or the other. I talked to a lot of folks from an environmental standpoint, and they say, thank goodness. Let’s push it lower.

And then, of course, we know those factions in the U.S. — Elon Musk, for example, sees the number and says, that is too low. It needs to be higher. So, it’s a great caveat for us to set out at the beginning that we might be talking about all kinds of numbers and places, but perception and feelings about those numbers, they go hand in hand with this.

How true is this statement? As countries get richer and more educated, their fertility rate drops.

If we’re trying to make it a causal statement, it is somewhat true and only partially, because we have some really interesting, huge examples where that has not been the case. And let’s take India, for example. So a lot of people do not realize that India is already really below replacement level for the whole country.

And what’s so amazing about that is, a lot of people may remember Paul Ehrlich opened his 1968 book, “Population Bomb,” by talking about a trip he made to India. And there were people everywhere, people on the streets, people eating, people drinking, people sleeping, people, people, people. And now, those people have a total fertility rate below replacement level. And India is not a wealthy country. So, it’s not the case that economic growth preceded declines in fertility rates because state policy can serve that interventionist role.

So, but I want to pick this apart because I hear you saying two things here. One is that you have countries that have not traveled all that rapidly up the education, income scale, though India has traveled somewhat up the education, income scale. I mean, there’s been a lot of development there.

But you have countries where you’ve seen a sharp fall in the fertility rate without a very sharp rise, let’s call it in median incomes. But I’m also asking a question slightly to the side of it. If a country has gotten richer, if I just tell you country A and do not name country A has gotten significantly richer, that country A is now highly educated, highly literate, it is wealthy, does that allow you to predict with a high level of certainty that country A is probably going to be low fertility rate, probably below replacement level?

It sure does. Yes.

Let me ask you why. Because this, to me, is the slightly mysterious thing at the heart of this conversation and my interest in it, which is I know it is a demographic fact that when you look around the world, rich countries, more educated countries have fewer children. It does not seem obvious to me that’s the way it would have worked out, right?

You have a world in which your kids are more likely to survive. You have more money. You can get more help. Your life is better. You can give them a better life. You can take them to Chuck E. Cheese, perhaps, as I’m doing later with my kid, who just turned five. Lots of things have gotten easier for you. And that might mean, oh, you can actually have more children, or certainly, a lot of children, right? But in fact, it goes the opposite way. So, why is that? Why does wealth lead to fewer children?

It sure does, and you are right that this is, in some ways, counterintuitive. Well, sure, we’ve got those rising income, rising education. We’ve also got shifting values and norms. And listen, I’m a political scientist. I’m a trained political scientist. We absolutely talk about values and norms. We also know that it’s really hard to measure some of these and it’s really hard to put them in a causal chain.

So, when I’m thinking about reflecting back on these big changes and looking at the literatures and looking at all the causality, that’s the one that I think has us where we are today. There’s been a tremendous shift in values and norms. And so, I think about my own life. So I have two children. And I have values beyond just wanting those children. Sorry to them if they listen to this. Thank goodness, they probably won’t, till they’re older. I do value my free time. I do value a nice meal at a restaurant. I value time with friends, time with my spouse, et cetera, et cetera. I value my career. And I value time with them the most. But you know what? It does compete for time.

And I think it’s that value shift, is that as we are more educated, as we have more income earning opportunities outside the home, as our standards of living rise, the number of children that we want shifts because it competes with other things that we want. I don’t know if you find that to be the case for you and your peer group as well. But that’s the case in my peer group, certainly.

I do find it to be the case. Is a different way of saying this that as countries become richer and more educated, they become more individualistic. And when you’re more individualistic, and people are making decisions more about their life, their self-expression, their set of choices, do I want to travel, do I want to become a PhD in political science, that, then, children are one choice competing among many?

I think that is part of it. And I think it’s just even more complicated than that. And I come back all the time to reflecting on the term “family planning” because family planning, in the greatest sense of it, is, you plan and decide when you get to have children. And when you can make that choice, it becomes really difficult to decide, is now the right time? Is now the right time? Will I be in a better position to do this in five years?

So, I think yes to the individualism, but I also think the literal logistics of it, deciding when to do something, it’s another kind of pressure that pushes downward. You kind of keep putting that off. As we know, as parents, there’s actually never an ideal time to do this. There’s always some reason not to. But I think it is that ultimate planning. We can’t leave that part out.

That’s an important corrective, I think, to something buried in my individualism hypothesis, which is that, as you were saying earlier, there’s a culture here. If I had told my parents — I met my partner when we were 24.

And if we had gotten pregnant at 24, that would have been, in the scope of human history, maybe even a bit on the late side. In the global picture, totally normal. And in the picture for college-educated, career-ambitious Americans, pretty unusual. And if we’d said we’re having kids at 24, a lot of people are like, you are? Did the birth control fail?

Exactly. Uh-oh. What’d you do wrong?

Right? That there is a culture around you of, when do things look normal? And also, we’d have been the only ones in our friend group with kids at that point. And so, there is this way in which, yes, there’s a lot of individualism, but the individualism also has very potent cultural grooves, right? You’re supposed to go and get education, and then more education and then more education, and then establish yourself in your career and be financially in a good spot, and of course, be married.

And by the time you’ve done all that, you might be 30. You might be 32. You might be 36. And even if you wanted to have three or four kids at that point, you do end up running, particularly for women, into a biological clock problem.

Yeah. So, for the total fertility rate for the U.S., writ large, is about 1.6 to 1.7 children per woman. So, it’s decently below replacement level there. For the more education you get, typically, the lower it is. It’s this success sequence that we talk about. OK, you’re going to raise your kids to say you’re going to get lots of education. Then you’re going to get a great job. You’re going to buy a home. You’re going to start a retirement account and get some savings and then have children. So, any little blip along that would then push that off more and more.

Something you mentioned that I think is very important is this idea that maybe within this larger individualistic culture and within this larger idea of a success sequence, there are pockets. So, last time I went on a first date, I was 19 years old. That’s because I met my husband then. I was engaged. I thought about this the other day. This sounds crazy, but at 21, on Valentine’s Day, I was 21 years old, and I was engaged. I’ve now been married over 20 years.

Congratulations.

Yeah, thanks, right? That probably sounds absolutely nuts to a lot of your listeners. But you know what? I was the last one of my friends to get married. We were college-educated women. Getting married early looked very normal in my group. In other parts of the country — I mean, I’m from the South, I’m sure you can hear — it is pretty normal to get married. And then you think about my neighborhood — got lots of folks with more than two children. So, what you’re surrounded by and how you kind of measure normal behavior, acceptable behavior, those cultural values and norms, they affect your decisions around dating, marriage, and having children.

I see this in my own world. And I am part of different communities. I’ve lived in, over the past 10 years, three different cities. My communities are typically pretty highly educated. But that has been different in different groups, too.

And it’s got me thinking about this question, which, what does it mean that the more sort of choice people are exercising, the more they’re putting into their careers, oftentimes, the fewer children they’re having, often to their sadness, right? I know a lot of people who wanted to have children. It’s not worked out for them. They want to have more children than they’ve actually been able to have. And there are, obviously, values in this whole conversation.

And I will say for myself which values I feel like I should even hold are unsettled. I think there is something more important about having children than simple choice. I think there is something about the continuation of the human species. I think there’s something about the connection to things and histories and generational chains beyond you. And also, I think it’s fine for people not to have children.

But in some ways, what worries me a little bit is, if you want to, quote unquote, “succeed in America,” you end up with fewer children on average. And if you imagine an America where everybody tracks the fertility rate of the highly successful, you’re looking at America with, as I understand it, a fertility rate that begins to look more like what you’re seeing in Japan, more like things people understand as rapid demographic decline.

There’s something here. I don’t know if we always describe it as values. It might just be what the success sequence muscles out. But I think, in some ways, it’s important to ask if that is leading to the right set of values, and for people, the right set of life outcomes.

Maybe, but I actually think there’s a little bit more to it because the gap with highly educated and less highly educated is not that big anymore. However, it is true that the longer you stay in education, the more you kind of truncate the years in which you might have children, so you might not have that second or that third child.

And I’m a little unsettled about how to talk about this publicly because you can tell someone what seems like a fact — hey, educated women in the U.S. might be having fewer children. What they do with that information is not up to you. So to the degree that we have perfect information, as a woman with a PhD, to understand that if I’m planning to finish my degree before I have children, then I will need to do so in this certain amount of time in order to make sure I’m still within that window of being able to conceive. That is one thing.

To try to limit someone’s rights in terms of education or change their pathway because you care about changing their total fertility rate is different. And actually, both of those conversations are happening right now among the elite in the U.S.

Is this really something that amenable to policy change, though? One of the things that is most striking to me about the data here — and here, I’m zooming back out to the international context — is that across many different kinds of societies, including some that have seen this as a crisis for their country for some time — I think here of Japan, I think here of South Korea — the ability to shift this through policy — and people have tried a lot of different things and a lot of different kinds of messaging and tax incentives and this and that — it doesn’t really seem like anything has worked.

This seems like something beyond what, at least, policy at the imaginable margins of things you could pass — you know, get a tax payment for having a kid, you get your income taxes knocked off, you get universal pre-K and child care and health care and the Scandinavian Social Security net, et cetera — it doesn’t really seem to do much. I mean, almost all these countries are converging downwards.

And in the most extreme cases — again, I think here of South Korea, which I believe is now below total fertility rate of one, so I mean, you’re entering geometric decline — they’ve not been able to turn that around. So, why doesn’t policy have more effect here? And what do you learn from some of the extreme cases, like some of these East Asian countries where this has been seen as a genuine threat?

You are absolutely right, yes. State policy is pretty effective at being able to take a high fertility society to a lower fertility one. It is generally pretty ineffective at a sustained rate. That’s why I say, this is a permanent shift for us. And so, as a researcher, we want to isolate a variable the same way that a policymaker does. If you can nail it down to the top one or two reasons why a fertility rate is low, then you could presumably put a policy in place to change that.

So let’s say, for example, we know, through research, that a lot of folks say that high child care cost is a factor in their decision-making around having children. So, a policymaker takes that information and says, all right, let’s work on subsidizing those centers, or I live in a school district that didn’t offer pre-K — that made a big difference for me financially. It would have been nice if they had offered pre-K.

But the state policy will fix that, and then people say, yeah, but also, the housing prices are really high. OK, let’s come in. Let’s talk about adjusting some mortgage rates or maybe give you some subsidies for that. Yes, but also — and it just kind of goes down the list. So, it is really hard to isolate a singular variable so that you can have a state policy. And that’s where we come back to, how do you isolate general values and general cultures?

Now, the extremes can tell us a little bit here. Throughout East Asia, which has a region with the lowest total fertility rate in the world, there is something in common. And I first learned of it when I was still an undergraduate, I think. And I actually think this is probably part of what set me into wanting to study this for the rest of my life. I studied Japan, and I remember trying to write this paper — this sounds so funny now. I think it was called like “Sex in Japan” was like my senior thesis.

And I remember learning about Japanese young women were basically being — they were being vilified, really, in the media for living this very individualistic life, rather than getting married to a man, settling down and having children. And I think now that I’ve matured in my scholarship and studied more about this, that was symbolic of an opting out.

And we see this opting out kind of running throughout East Asia. South Korea has something called the four no’s — no dating, no sex with men, no marriage, no childbirth. And so we see them have the lowest fertility rate in the world. It’s this idea that marriage is no longer required to have a good life. You can have a job. You can make money on your own. And in fact, it is not only no longer required, it might actually stifle your life because of gender relations within the household.

South Korea has paternity leave. So, there you go — state policy, right? Oh, you say there’s no maternity and paternity leave. Let’s give you that policy. But men do not take the paternity leave. And that’s the values and cultural norms there. So, those are very important in being this counter or a limit on state policies’ ability to affect change. So, there may be ways — this may be where research needs to go. How do you change culture if you want to through state policy?

Tell me about a couple of the examples here in some depth. So, there is the, I think to many people, to me, horrifying example of Romania. That sits out there. You write about it in your book. Then there’s also — I mean, as you know, Japan has done things. South Korea has done quite a bit.

Hungary recently has been trying to increase its birth rate. You’ve seen things in Scandinavian countries, as they build out a more gender equitable form of parenting. So, tell me about what examples stand out to you. And then were any of them effective in the long-term?

So, a lot of people who are aiming to raise fertility rates are trying to raise them to replacement level. And the reason they’re trying to do that is because it just gives you this nice stovepiped age structure where you got a steady number of people being born, aging into the workforce and aging out, without any strains on needing to scramble to build kindergartens or scramble to pay for Social Security.

But we really don’t have societies that hang out there at replacement level. Once they tend to fall below it, they tend to stay there. And if they are trying to get back to this elusive replacement level, we just don’t see that. And so a couple of things to talk about with this.

One is, how do you get a population, if you’re a state, to have fertility rates that go back up above replacement level? Well, you can strip away individual rights. I am not advocating for this. But we have an example of that. Nicolae Ceausescu in Romania, he said, I want more Romanian babies specifically. And fertility rates were already low there.

So, what did he do? He kind of did the inverse of what China did during some of its population policies, like the one child policy, taking away contraception and making sure women couldn’t get access to legal abortion. And you did see births increase there. You also saw maternal mortality increase. You saw a lot of societal issues there.

And it was only up as long as his thumb was pressing on it. And as soon as he’s gone, it goes back down. So, no, we do not really have examples where a society goes way below and then comes back up to above replacement level and hangs out there, and everyone is happy.

A little side note here that I think is interesting — and this is really important for us to talk about. It used to be, like when I started my career, that we would talk about low fertility societies, we were talking about democracies, for the most part. Now, a quarter of our aged countries are non-democracies. So, I actually think it’s really important for us to integrate that into the conversation because it is easier to restrict rights in a non-democracy. So it is something I worry about a lot.

Well, we have a current example of this. Russia’s fertility rate is not particularly high. And one of the things Putin said often, before invading Ukraine, was that Ukraine was full of what he considered to be Russian babies, Russian people. People are, to him, power. People are understood, in many countries, to be power.

And Ukraine, as Putin understood it, was taken from Russia, taken from greater Russia. And he was going to get it back and get back all these people and get back all these children and get back all these babies. This was an articulated rationale. And it’s hard to parse exactly what led to Putin invading, but this seems to be one thing in the mix of his considerations.

I think so, too. And I think at the end of the day, why this matters is that people look at shrinking populations, of which there are already over 30 countries that are shrinking, and low fertility as an existential issue. And so, when you elevate it to an existential issue, the question becomes, what are you willing to do to change it?

Let me put aside the language of existential because I think people’s minds shut down when you begin to get into whether something is an existential threat, but is low fertility a threat? I mean, I look at South Korea, I look at Japan — I think of that certainly as a significant problem for those societies. I mean, within 50 or 60 years, their population level will convulse downwards.

Whether a fertility rate of 1.8, 1.6 is a threat, I don’t exactly know what to think about it. But there’s certainly an intuition that you would be a stronger country if you were a 2.2 or 2.4 or 2.5 than if you were at 1.4 or 1.2. How do you think about this?

I mean, you told me today. You’re giving talks there on national security. You’re giving talks on demography and finance. Presumably, those are people worried about chaos emerging from this and either thinking about how to defend against it or profit from it. So, what are you telling them?

If we zoom out on the whole and we look at how globally fertility rates have fallen from way high, six, seven children per woman, down to now two, this is a positive story. It’s something that we worked for, for decades.

How wonderful now that we can have fewer children and feel confident that those that we have will make it to reproductive age, because that’s really what happens, right? That that’s how societies do this demographic transition from high to low fertility. So, we should celebrate, generally speaking, getting to replacement, or I think even just a little bit below replacement level. I don’t raise an alarm about that. I do, however, feel alarmed when it is super low. And here’s why.

If you just told me about a hypothetical country — you said country X — their total fertility rate is seven children per woman, and that’s the only thing you told me, I could paint a picture of that country for you. And I could tell you a lot of things about that country that were probably not great. I would say, probably women and girls are not being educated. Probably, there’s not great health care. Probably, there are no jobs. And probably, it has poor governance.

On the opposite end of the spectrum, if you told me that a country had a fertility rate hovering around one child per woman, to some degree, I think that also reflects that there are some things in that society that are broken, that people, women particularly, although I always do hate to put it on the shoulders of women, but in those low fertility societies, it seems to be the case that women are not willing to reproduce the current social structures. They are not working for them to a huge degree, to the point that they are willing to opt out of this idea of marriage and having children, and seek a different path for themselves.

So, while low fertility, generally speaking, I think is a positive example, super low fertility is something we need to understand much more to say, does it reflect that people are not optimistic about the future? I mean, having kids is the ultimate faith that the future will be good. And we saw it go low around this time the Soviet Union collapses in Eastern Europe. People feel dismal about the future, and they don’t want to have children.

Or can people just not afford to buy a home at younger ages? Do they feel like they are isolated and insecure themselves? And if that’s the case, then those are things in society that we would want to fix, no matter what. And perhaps, the side effect, the positive externality for this will be that fertility will go up. But just trying to change that number doesn’t actually change why that number might be incredibly low in the first place.

I struggle a little bit with this question of pessimism and fertility. And so I’d like to open it up a little bit. I hear a lot from people who say they don’t want to have children because of climate change or because the world is chaos, and it’s been terrible here, and how can you bring a child into this?

And I always think when I hear that, that while there’s truth to the many, many, many, many, many problems that we face, which is primarily what this show ends up being about week after week, the people having this conversation are, to a first approximation, the best-off people in the entirety of human existence. And what it was like to be in this world in 1940, in 1810, in 1700 and 1500 and going all the way back where you have child mortality at levels that we can’t even conceive of now.

Going back to this idea that bringing children in is an act of optimism, the people having more children right now, they’re in Afghanistan, they’re in Nigeria, they’re in sub-Saharan Africa, as you mentioned. These countries are doing differently than each other. I’d much prefer to be in Nigeria than Afghanistan, but they’re not as rich as post-grad degree Americans.

And so, there’s something here that I find odd, kind of that makes you wonder, sometimes, if it’s not backwards justification, right? People for whom their children and them would have much more comfortable lives come to see it as so uncomfortable, so impossible, so riven by inequality, that they can’t imagine making that choice, and people for whom their lives are much more that way — they do live in societies with much more pressing levels of poverty, of war, et cetera — don’t see it that way. So, how do you think about that?

Well, a little caveat to that — a lot of these places that are war torn, they don’t have contraceptives, and they are not able to actually make those choices. They don’t have full choice about their reproduction. I think it does come back to this idea about choice. And I will also say that, I mean, I’m in the same bubble you’re in. I spent 15 years in academia, for example. It’s a certain set of people who just might justify their choices based on that.

If we pull out of that group a little bit, you don’t hear people talk as much about, I’m not having a child for the sake of the environment, et cetera. So I do think that is not really indicative of the U.S. as a whole. Part of the reason the U.S. total fertility rate is low is that teen birth rate is down. So, isn’t that something we worked for, for a long time? So there’s a little bit of complexity here as well between, at what point are you asking somebody how many children they want, at what point in their own lives are they, and how much can you really trust that?

So right now, I basically would not be able to have kids anymore. But if you asked me how many would you have liked to have had, I might say three. Part of why I say three is because my first two were awesome. They’re healthy, and they are also nine and 11 years old, which my friend says is the sweet spot between diapers and drugs. So they’re highly pleasant children right now.

I think to myself, I could have had a third. I would have totally nailed that. But if you had asked me that when I had 202, then I would have given you a very different answer. So, some of this is also a measurement issue for us. So we don’t know somebody in their 30s — they’re not done yet. We can’t actually find out about them. We’re learning about completed cohort fertility for people born in the 1970s right now. So, we’re always a little bit behind in our knowledge.

One other question that tracks this wealth issue is that a lot of people who are doing very well by global standards, maybe not the richest one percent of Americans or anything, but one thing you’ll hear is that it’s extraordinarily expensive to have children. And I have two children, and I’m here to confirm that it is extraordinarily expensive to have children. But of course, the people having more children are poorer.

And this seems to, though, be validated in the data that as you get wealthier, the expectations on how much you will spend on your children change, what it means for your children to keep up culturally, educationally, economically. That’s not just true for money — it’s true for time. The amount of time that more educated parents spend with their kids is really high. And it’s both beautiful.

I mean, I really treasure a lot of the time I spend with my children. And it is difficult. I don’t think people parented this intensively when they had five or six or seven kids. How do you think about that, that question, the way parenting has become both capital and time intensive, as people get more capital and time to invest in it?

I think that is a huge factor in why people have fewer children in the U.S. It obviously isn’t just money because we all have more money now than we did. We’re doing better. And so, you can’t just nail it down to say it’s expensive. It really is about this intensity. And some of that intensity is money. I’ve got friends with kids on travel baseball teams — oh, my goodness — a lot of money and a lot of time.

I just pray my kids don’t play club sports. Like, that’s the only thing I truly want as a parent, for them to not be very good at sports.

Yes, mine inherited my lack of ability, so I am winning. Yes, they’re like, can we go to the library? I’m like, you bet, sweetpea. Let’s go. Because, yes, it is just that. And one of my friends, the same one, she’s a stay-at-home mom, and she says, how do people do it if they both work? And the answer is, of course, you either have some prescriptions for some anxiety medication thtat you both pop in the morning, you try to get some help, but it’s hard to get help. People don’t live near parents who can help them, et cetera. It is just such tremendously intensive parenting. And that is the culture now.

We also see that in East Asia, by the way. So a lot of this, you do see globally. South Korea, politicians have talked about trying to change some of the requirements on entrance exams to pull away some of that pressure that each child must just be raised so intensively and perfectly in the hopes that that would then change the culture around the number of children that you have. So, yes, I think it makes a huge difference. There are only a certain number of hours in the day.

But I also think that there is a lot of negativity about parenting that’s shared in social media. Most of the stories shared about parenting online are, oh, my gosh, my toddler got into my makeup today. And I have to record a podcast with Ezra Klein. And what am I supposed to do? And I hear it a lot among 20 somethings and 30 somethings that you sure don’t make it look like it’s a lot of fun.

And during the pandemic, when all of us were on these video calls and our kids were screaming and streaming in, in the background, it didn’t make it look like it was going to be a very enjoyable enterprise because you don’t see a lot of the beautiful moments. So I think that’s in there, too. That’s in that mix.

I think a lot about this particular question because I’m so caught on it. Because on the one hand, I get the all joy, no fun theory here. And I don’t find it to be true exactly. I find there to be a lot of fun in it, but I’m also somebody with a pretty flexible job. I work a lot, but I have a fair amount of control over those hours. And I’m somebody with enough money to fill in some of the gaps that we need to fill in. So, we can go out occasionally, that kind of thing.

And the thing that keeps coming to mind for me is like this collision between two ideas. One is that maybe the way we’re doing it, it’s not that much fun. Maybe the amount of pressure we’re putting on ourselves — is my kid reading early enough, are we spending enough time together, are the weekends enriching enough — my whole weekend is planned around what might be good for my kids. It’s like playground, library, go and get a bagel, right? It’s just, it’s all kids all the time. It’s not my sense that that’s how it’s always been.

And then on the other hand, it’s not also my sense that it was always fun, that maybe it just wasn’t part of the choice structure the way we thought about our lives that everything was about how much fun it would be, how individually enriching it would be. So there is this kind of interesting question of, one, have we made it less fun than it should be? Have we made —

In a way, are we too pro-natal for society in a way that has made us low-fertility societies? Because now what it means to be pro-child is to treat your children so well you can’t imagine having more than two or three of them. And on the other side, that this question of making everything a choice about is it going to be fun for me, I mean, when you look back in human history, that’s always how we thought about things.

Yes, and we have some data on this. The one that always strikes me is that a working mother today spends more time with her child than a stay-at-home mom would have a few decades ago. We’re spending more time with our kids on average. So I absolutely think that’s the case. And I do think it matters.

This very indulgent sense that everything should maximize your pleasure, why? Why is that the case? And so, every moment as a parent is not the best in the world, but overall, I don’t know. I’ve not seen a study, like, are you sad you had your kids? I mean, probably somebody has done that. Do you wish you hadn’t had them? It’s very few people.

Something that has come up a few times here is simply that women work now. And nobody wants to go back on that, or at least, I don’t want to go back on that. But how much is that just an explanatory factor, that this idea that you’re going to have high fertility in societies where you have dual income, full-time working parents, but also there’s nobody else to take care of the kids, that that just doesn’t fit. I mean, you can say whatever you want. You can do whatever you want. You can have your tax incentives, whatever. But if you’ve got two parents working, it’s just pretty tough, particularly if they’re not making millions of dollars at their jobs.

And it’s extra tough when you don’t have a community that supports you. And I think that may be one of the biggest differences now, is that if I think about — I work a highly flexible job. My husband works a less flexible job. So we have a two-income family. But anything I need for support, I’m basically hiring out. I mean, there’s spreadsheets for if I have a work trip. OK, this one’s coming on this day. This one can’t drive. So this one has to do this, that, and the other.

We don’t have community support. That is different than saying, do you have a daycare that opens at 7:00 a.m. or 6:00 a.m. for you to drop off, and how late is pickup? The idea that you are living in a community with neighbors who almost have this communal sense of parenting is probably way too much of a phrase there, but just this supportive structure around you that’s outside of policy.

And I wonder, too, not just about the parents, but the other kids. I mean, I didn’t grow up in the long, long days ago. It still feels fairly recent to me. But I did grow up at a time — I grew up in suburban California. There are kids in almost every house on our block, and they all played outside. And they all just kind of ran around as a pack. And there were younger ones and older ones and everybody played kickball on the garages.

And it wasn’t that it was idyllic or not idyllic. And for all I know, I’m remembering this wrong. But also, whenever I just read older accounts of families, it’s like the kids are just running around. And there are other kids, and the kids take care of the kids. And in big families, the older kids take care of the younger kids.

And so, there’s this one issue of how supportive the community is and this other issue of whether or not there is this almost independent kid society, because if there is an independent kid society and the only way to create kid society is that you’re on your phone G-caling a play date with this other family from school, and no, we’re not free on Sunday, but what about three Sundays from now, then the parents are involved in every part of that, whereas it seemed my experience and from other things I can tell, that there was a little bit more of just an autonomous thing happening for kids at another point.

Yes, and I do think that makes a difference. I really do. My husband grew up in upstate New York, and he talks all the time about how he and his friends, guys in the neighborhood who were his same age in school and some a little bit older, would get on their bikes, they’d go into the woods, they’d be gone all day long, and nobody thought anything about it. And if one of our sons wants to go over to his friend’s house and he wants to ride his bike, we’re terrified to let him.

Now, part of this is where I live. Statistically, maybe you should be a little bit terrified to let him go, but probably don’t need to be quite as terrified as I am now. But there’s a sense that what if something happened? I would never forgive myself. What will other parents think if I just let my child go out because — and cross a major road. It really is a different intensity to parenting. I did not grow up in a neighborhood. I grew up in the countryside, and I grew up as an only child. But I was completely independent, and my mom wasn’t saying, OK, you have now played with that litter of puppies for too long. Perhaps you should come inside and eat a snack, or just really micromanaging my life there. And I totally am doing this to my kids. I try not to. I get that I shouldn’t.

But I think to myself, hm, have they done that activity too long? Perhaps, I should do this. And yes, you would like to go to a friend’s house? Let me text them for you because I’ve heard it’s bad for you to have phones. So, that means that I’m the person in charge of scheduling all of this for you because I’m scared for you to walk out of the house and be on your own.

So, yes, it is just a super intense parenting without this community and this autonomy. And that definitely can play a role in maybe you don’t go from one to two kids or from two to three kids. Because that’s another part of this. I think sometimes when we talk about low fertility, we think about having kids or not having kids. But there’s also the, do you have one, two, three, et cetera, and how that changes over time as well.

Yeah, to add numbers to that, I think the United States, you mentioned earlier, the fertility rate is about 1.6 — any of these surveys showing that Americans would like to have, on average, 2.7 kids. So, there’s this question of people who don’t want to have kids that gets a lot of attention, but there’s also this question of people who would like to have more children than they do.

And for one reason or another, it doesn’t work out for them, or it’s not possible for them. And that feels like a place worth putting a lot of attention into because I think everybody’s most comfortable and correctly so with having a choice and rights-based approach here. And if people could have closer to the number of kids they wanted, that’s making everybody better off, in a way.

Yes, and there’s all kinds of little things about this. And we did have two. And we talked about if you had a third, where does the car seat go? We would have to get different cars to be able to fit a third car seat because our kids were close together. I have an 11-year-old son who is not a small guy. He’s a tall guy, 90th percentile. He’s still sitting in the back seat. He’s not supposed to sit in the front seat yet of the car. And that means that only one of you can have a friend come play today if we’re going to drive you anywhere.

So, there’s just these little logistics. That difference in going from two to three is big. I have a friend who has a blended family that ends up with three kids. When they’re all together, she’s like, we have to have a different table at the restaurant. We have to have two hotel rooms. Those kinds of things, I think, do shape people’s decision-making around going from two to three kids. And then, of course, add the cost in there.

Depending on how old your kids are, how close together they are in ages, you could be paying for daycare/pre-K for three at once or college for two or three at once. It’s something that can really make a difference for how many you decide to have.

Is that a way that low fertility rates end up feeding on themselves? I lived in San Francisco, which is notoriously a quite low fertility rate major American city. And you could just feel it. You could just feel that there was not infrastructure, really, for kids. I mean, there were some playgrounds, but nothing opened early. But kids get up early. And it’s all these little things that just make it a little bit harder.

And it’s not that people are being jerks about it. It’s just that infrastructure, commerce, culture adapt to what is around it. And the more what is around it is families with very few kids or no kids, the more it tunes itself for them. I mean, this is very broadly observed, but there’s been like a big trend against restaurants really having that many reservations. And if you don’t have reservations, you’re not going there with kids because the kids are not going to sit around outside waiting for a table.

And there’s just a lot of little things like that that I feel like whenever I travel to societies with high birth rates, you really notice that they feel different. The whole thing just looks different in more ways than I can catalog, but in ways that, then, when I come back, you’re really like, oh, I live in a low birth rate society. Like, that becomes a clear thing to you.

And I think what’s remarkable about this is that there’s such a divide between rhetoric and action on this. So, in the U.S., the conversation is starting to trend toward, OK, we are a low fertility society. Uh-oh, how do we change that? That’s the rhetoric, but the question we need to ask about the action, then, is, are we really a society that values children and families? And I think in a lot of cases, the answer really is no.

I mean, I remember reading an article maybe a year or two ago about a town in Japan. It was a small town, but they were having a baby boom, so to speak. And of course, this is in a setting where fertility rates have been low for decades and one of the oldest countries on the planet. And they started trying to talk to this mayor about what are you doing differently, and the answer really was, we value and integrate children and families into everything here.

It’s not a policy, so to speak, like the kind that you might think about for policies to raise fertility. It’s a feeling, and it’s action around that. I think that’s part of why we see who’s having babies even within low fertility societies, it’s religious communities and emphasis on community.

So, within a church group, for example, if you go to a Wednesday night service at maybe a Protestant church, there is something for the kids. Someone is taking care of them. You don’t have to worry about it. You can go to your adult formation class, speak with other parents, and you’re not worried about where are the kids.

They probably have dinner there for you so you don’t have to plan that, cook that, clean that up. Kids can run around, scream, and it’s OK. And so, we do tend to see higher fertility rates among societies that are highly religious. And I think that community part plays a big role in that.

I was going to ask you about religion as an independent variable here. How much is religion a force that is capable of changing fertility rates? And on the flip side of that, how much is secularization a driver of pushing them down? Are there, in fact, any secular societies or large secular groups that have high fertility rates?

Well, I think generally speaking, religiosity matters. It’s about how religious are you, which we think about, how often do you go to services or pray, et cetera. For example, Mormons in the United States have a higher total fertility rate than those who are not religious at all, have low degrees of religiosity. And we tend to see that around the world. Secularism? Yeah, we do see lower rates there. And since we know that religiosity itself is declining, we would expect that to be a pressure towards lower.

But I think context does matter. One of the puzzles that we talk about in demography a lot is looking at Israel. And I think there’s a lot there in terms of exceptions, but a lot of folks know — and I write about this in the book — that the ultra-orthodox community in Israel has high fertility rates. But the secular Jewish community has higher than you would expect, given peers. And so, they kind of hover around replacement level.

And so, they are within this context of higher fertility rates. They’re within communities where there would be more children. And maybe, maybe that’s pressuring them higher. It gets hard for me with religion when I try to parse out these different things because there’s a lot going on with, how do you think about the future, or how do you think about the afterlife? How do you think about the purpose of why we’re here on Earth? And religious teachings do come into play there.

So, when we kind of contrast that with a lot of folks I talk to in the environmental movement, they say, we shouldn’t have humans at all because it’s bad for the planet. I mean, these are extremists, but they’re in my email inbox. And so, that’s two really different worldviews about the value of children, the value of people, the purpose of it all.

Behind both of those worldviews is not just values, although values are probably there, but also an instrumental sense of what will happen in different scenarios. So the overpopulation folks, they’re worried about the human load on the planet. More humans is, to a first approximation, more carbon dioxide, more material usage, more humans taking up habitat, eating livestock, or raised on arable land, et cetera, et cetera. Then, there’s the other side, right, the people on the right. And there are people who just believe children are an intrinsic good, that either it is a religious duty or just a beautiful thing, right? More souls in the world, more human beings who can have important, meaningful human experiences. But there’s also a view that sharp demographic decline is a catastrophe from a power and social stability perspective.

So Brink Lindsey from the Niskanen Institute writes about low fertility societies. Quote, “Whole societies will soon start to melt away. As with our personal ties to each other, our ties to the social order are weakening as well. Trust in virtually all social institutions is in relentless decline.” What he’s seeing is a world that is going to fall into a kind of chaos because relatively close to replacement rates, societies are stable, and those booming up maybe are not, but also those plummeting down are definitely not. So, how do you think about the instrumental dimension here?

I think that arguments like that suffer from a significant failure of imagination because what they’re basically saying is that you either grow infinitely, or you collapse. And there is nothing in between. And I think there’s a lot in between. We just love to be alarmist about population. We’re alarmist about it being too high in the ‘60s. We’re alarmist about it being too low today.

And so, I don’t like those arguments that say, well, if we don’t have children, societies will completely fall apart. No. They will change, but they change all the time. Sometimes they change for the better, sometimes they change for the worse. If I lived in a retirement community in Florida, I wouldn’t say, how awful are all these people and myself. What a shame that we’re all happily down here with the sunshine. Isn’t life terrible? It just looks different.

Let’s be clear here — why does low fertility matter? We’ve talked a lot about kids. But at the end of the day, it’s because it eventually leads a population to age more older versus younger people. And then — thanks, math — it will shrink. So people equate that with individual aging. And we have a terrible, pessimistic, fearful view of individual aging. And because we kind of overlay that onto population aging, how could it possibly be a nice world?

One of the other — I don’t know whether to call this a concern or prediction — maybe both — is that you’re just going to see a huge shift in world power as population rates change. So, places with more population are, over time, going to become more powerful. Places with less are going to become less powerful. I think certainly at extreme levels, that is true, right? South Korea is falling by half generation after generation, or more than half. I do think it is going to see its power and sway reduced, and I think it is going to be in more danger from neighbors.

And then even within societies, right, there’s this question of who is gaining power in it. So I mean, it’s a common concern, or at least, observation in Israeli politics, the very high birth rates of the Orthodox have made the Orthodox faction in Israeli politics much more powerful, which has swung Israeli politics to the right. So, how do you think about that way in which, over time, this leads to who has numbers, and thus sway, in society?

I think it’s a very different answer depending on if we’re at the global level or a subnational level. So, if we’re at the global level, does population equal power? Nah. You know why? Because the rules of the game are already written. So, I tend to be an institutionalist in political science terms, meaning I think a lot about the power of the structures, the power of the rules of the game.

And guess what? Those are so set in stone right now that to think about India challenging those rules, challenging that order, it’s not going to happen in the next couple of decades, even though they’re now the most populous country on the planet. But I think there’s more to it at the subnational level there. But again, I’m an institutionalist.

So what are the rules of the game? If you have rules of the game that allow small interest to take political power, say, for example, the type of parliamentary system where you could get 20 percent, 30 percent of the vote and come to rule in the country, then population sizes will matter in one way. They can potentially take over that governance there.

I think in the U.S., it’s more complicated because we’re still on these two parties, and we’re not getting out of these two parties anytime necessarily soon. So it is harder for a particular niche, even though they may be growing in population, to take over the whole political governing landscape.

You said at the beginning here, look, there are all these facts, and then there’s what you do with the facts. What do you do with them? We’ve talked a bit about the sort of overpopulation take. We’ve talked a bit about the decline of state. What is your orientation? When you look at societies at 1.5, 1.6 — they seemed to be dropping a little bit lower over time — how do you think about it, and how do you think about where they should be thinking about going from here?

I’m trying to strike a balance between showing how important it is to always view demographics, but not so important that you’re willing to take away people’s rights or focus solely on that number, those population numbers, to the extent that you forget to deal with the people who are there.

So, I would think, for example, if we know climate change is happening, it would be a mistake to focus only on stopping or reversing climate change. While the waters are rising and coming above your front porch, you should also probably put your house up on some stilts. We need to think, instead, about how to have resilient societies that adapt to what is there.

And knowing that I’ve already said we should pay attention to super, super low fertility rates, if we’re just hanging out at this below replacement level, but steadily, this 1.5 to 1.9 area, this is not doom and gloom. It’s only doom and gloom if you are not willing to change anything else about it.

Do you have a pay as you go entitlement system, where you need a constant influx of workers to financially support those who are exiting the workforce? Well, then, yeah, you’re right. Probably is going to be doom and gloom. So, we have to have that adaptation now. We have to have that resilience. And we are wasting time and resources in not doing that, and instead, trying to put the genie back in the bottle.

But at the same time, I do think it’s important for us to support families and children for the purpose of supporting families and children because, otherwise, what is the point of it? What is the point? I think society is made up of us as people.

And a lot of times, with these aggregate numbers we talk about, we forget that they are just an aggregate of a bunch of individual decisions. And I want to live in a society that is optimistic about the future, where there are children and older people and people of working ages. And to me, that’s really the point of it all, is to see us as humans, as valuable.

I think that is a good place to end. And always our final question, what are three books you’d recommend to the audience?

So I have three books that I love for different reasons. So I love “Extra Life” by Steven Johnson. It’s zooming out to say, isn’t it remarkable that we have basically added an extra life because of how much we have improved health and life expectancy?

I also love this book by Paul Sabin that I used to teach out of. And it is called “The Bet, Paul Ehrlich, Julian Simon, and Our Gamble Over Earth’s Future.” And what I like about it is, there was not always the case that there was this huge divide between the left and the right over environmental ideas in the US. There was a time when we were kind of united on that. And it just kind of traces that history of how we came to be divided on this issue of overpopulation that I think is a really important thing to chronicle, and it’s a really interesting book.

And then the third one, it’s more of an academic book, but if you want a history of some of the most interesting, demographic engineering that we’ve seen in the world, it’s a book called reproductive states. And it’s edited by Rickie Solinger and Mie Nakachi. And it’s “Global Perspectives on the Invention and Implementation of Population Policy.”

And I think it’s fascinating to look at how individual countries will have this very anti-natalist policy for decades, and then fertility will go low. And they’ll freak out, and then they will put into place pro-natalist policies, just like a flipping a switch, you know? But you can really see these interventions over time in different countries. And it’s a really fascinating view into countries like China, India, et cetera.

Jennifer Sciubba, thank you very much.

This episode of “The Ezra Klein Show” is produced by Rollin Hu. Fact-checking by Michelle Harris with Kate Sinclair and Mary Marge Locker. Mixing by Isaac Jones and Efim Shapiro. Our senior editor is Claire Gordon. The show’s production team also includes Annie Galvin, Jeff Geld and Kristin Lin. We have original music by Isaac Jones. Audience strategy by Kristina Samulewski and Shannon Busta. The executive producer of New York Times Opinion Audio is Annie-Rose Strasser. And special thanks to Sonia Herrero.

EZRA KLEIN: From New York Times Opinion, this is “The Ezra Klein Show.”

JENNIFER SCIUBBA: Thank you so much.

EZRA KLEIN: So, tell me what the total fertility rate is.

JENNIFER SCIUBBA: So, the total fertility rate is — let’s just say it’s the average number of children born per woman in her lifetime. It’s a great measure because in one number, you can kind of get a snapshot to compare across time and across places.

EZRA KLEIN: So, when I listen to the conversation about total fertility rates, there are sort of two conversations right now happening at the same time. One conversation that I sometimes hear on my left — I get a lot of it in my email inbox for this show — is that it’s way too high. There are too many people. There are going to be even more people.

JENNIFER SCIUBBA: Well, you nailed it. That’s exactly what the conversation looks like. It’s like Goldilocks is in the room here with us, right? It’s either too high or it’s too low, and it’s never just right. So while we are perceiving this on the left and right in the world today, I will say that it’s, as a student of population history, it’s kind of been like that for a long time, this perception about global fertility.

EZRA KLEIN: How true is this statement? As countries get richer and more educated, their fertility rate drops.

JENNIFER SCIUBBA: If we’re trying to make it a causal statement, it is somewhat true and only partially, because we have some really interesting, huge examples where that has not been the case. And let’s take India, for example. So a lot of people do not realize that India is already really below replacement level for the whole country.

EZRA KLEIN: So, but I want to pick this apart because I hear you saying two things here. One is that you have countries that have not traveled all that rapidly up the education, income scale, though India has traveled somewhat up the education, income scale. I mean, there’s been a lot of development there.

JENNIFER SCIUBBA: Oh, yeah.

EZRA KLEIN: But you have countries where you’ve seen a sharp fall in the fertility rate without a very sharp rise, let’s call it in median incomes. But I’m also asking a question slightly to the side of it. If a country has gotten richer, if I just tell you country A and do not name country A has gotten significantly richer, that country A is now highly educated, highly literate, it is wealthy, does that allow you to predict with a high level of certainty that country A is probably going to be low fertility rate, probably below replacement level?

JENNIFER SCIUBBA: It sure does. Yes.

EZRA KLEIN: Let me ask you why. Because this, to me, is the slightly mysterious thing at the heart of this conversation and my interest in it, which is I know it is a demographic fact that when you look around the world, rich countries, more educated countries have fewer children. It does not seem obvious to me that’s the way it would have worked out, right?

JENNIFER SCIUBBA: It sure does, and you are right that this is, in some ways, counterintuitive. Well, sure, we’ve got those rising income, rising education. We’ve also got shifting values and norms. And listen, I’m a political scientist. I’m a trained political scientist. We absolutely talk about values and norms. We also know that it’s really hard to measure some of these and it’s really hard to put them in a causal chain.

So, when I’m thinking about reflecting back on these big changes and looking at the literatures and looking at all the causality, that’s the one that I think has us where we are today. There’s been a tremendous shift in values and norms. And so, I think about my own life. So I have two children. And I have values beyond just wanting those children. Sorry to them if they listen to this. Thank goodness, they probably won’t, till they’re older.

I do value my free time. I do value a nice meal at a restaurant. I value time with friends, time with my spouse, et cetera, et cetera. I value my career. And I value time with them the most. But you know what? It does compete for time.

EZRA KLEIN: I do find it to be the case. Is a different way of saying this that as countries become richer and more educated, they become more individualistic. And when you’re more individualistic, and people are making decisions more about their life, their self-expression, their set of choices, do I want to travel, do I want to become a PhD in political science, that, then, children are one choice competing among many?

JENNIFER SCIUBBA: I think that is part of it. And I think it’s just even more complicated than that. And I come back all the time to reflecting on the term “family planning” because family planning, in the greatest sense of it, is, you plan and decide when you get to have children. And when you can make that choice, it becomes really difficult to decide, is now the right time? Is now the right time? Will I be in a better position to do this in five years?

EZRA KLEIN: That’s an important corrective, I think, to something buried in my individualism hypothesis, which is that, as you were saying earlier, there’s a culture here. If I had told my parents — I met my partner when we were 24.

JENNIFER SCIUBBA: Exactly. Uh-oh. What’d you do wrong?

EZRA KLEIN: Right? That there is a culture around you of, when do things look normal? And also, we’d have been the only ones in our friend group with kids at that point. And so, there is this way in which, yes, there’s a lot of individualism, but the individualism also has very potent cultural grooves, right? You’re supposed to go and get education, and then more education and then more education, and then establish yourself in your career and be financially in a good spot, and of course, be married.

JENNIFER SCIUBBA: Yeah. So, for the total fertility rate for the U.S., writ large, is about 1.6 to 1.7 children per woman. So, it’s decently below replacement level there. For the more education you get, typically, the lower it is. It’s this success sequence that we talk about. OK, you’re going to raise your kids to say you’re going to get lots of education. Then you’re going to get a great job. You’re going to buy a home. You’re going to start a retirement account and get some savings and then have children. So, any little blip along that would then push that off more and more.

EZRA KLEIN: Congratulations.

JENNIFER SCIUBBA: Yeah, thanks, right? That probably sounds absolutely nuts to a lot of your listeners. But you know what? I was the last one of my friends to get married. We were college-educated women. Getting married early looked very normal in my group. In other parts of the country — I mean, I’m from the South, I’m sure you can hear — it is pretty normal to get married.

And then you think about my neighborhood — got lots of folks with more than two children. So, what you’re surrounded by and how you kind of measure normal behavior, acceptable behavior, those cultural values and norms, they affect your decisions around dating, marriage, and having children.

EZRA KLEIN: I see this in my own world. And I am part of different communities. I’ve lived in, over the past 10 years, three different cities. My communities are typically pretty highly educated. But that has been different in different groups, too.

JENNIFER SCIUBBA: Maybe, but I actually think there’s a little bit more to it because the gap with highly educated and less highly educated is not that big anymore. However, it is true that the longer you stay in education, the more you kind of truncate the years in which you might have children, so you might not have that second or that third child.

EZRA KLEIN: Is this really something that amenable to policy change, though? One of the things that is most striking to me about the data here — and here, I’m zooming back out to the international context — is that across many different kinds of societies, including some that have seen this as a crisis for their country for some time — I think here of Japan, I think here of South Korea — the ability to shift this through policy — and people have tried a lot of different things and a lot of different kinds of messaging and tax incentives and this and that — it doesn’t really seem like anything has worked.

JENNIFER SCIUBBA: You are absolutely right, yes. State policy is pretty effective at being able to take a high fertility society to a lower fertility one. It is generally pretty ineffective at a sustained rate. That’s why I say, this is a permanent shift for us. And so, as a researcher, we want to isolate a variable the same way that a policymaker does. If you can nail it down to the top one or two reasons why a fertility rate is low, then you could presumably put a policy in place to change that.

EZRA KLEIN: Tell me about a couple of the examples here in some depth. So, there is the, I think to many people, to me, horrifying example of Romania. That sits out there. You write about it in your book. Then there’s also — I mean, as you know, Japan has done things. South Korea has done quite a bit.

JENNIFER SCIUBBA: So, a lot of people who are aiming to raise fertility rates are trying to raise them to replacement level. And the reason they’re trying to do that is because it just gives you this nice stovepiped age structure where you got a steady number of people being born, aging into the workforce and aging out, without any strains on needing to scramble to build kindergartens or scramble to pay for Social Security.

EZRA KLEIN: Well, we have a current example of this. Russia’s fertility rate is not particularly high. And one of the things Putin said often, before invading Ukraine, was that Ukraine was full of what he considered to be Russian babies, Russian people. People are, to him, power. People are understood, in many countries, to be power.

JENNIFER SCIUBBA: I think so, too. And I think at the end of the day, why this matters is that people look at shrinking populations, of which there are already over 30 countries that are shrinking, and low fertility as an existential issue. And so, when you elevate it to an existential issue, the question becomes, what are you willing to do to change it?

EZRA KLEIN: Let me put aside the language of existential because I think people’s minds shut down when you begin to get into whether something is an existential threat, but is low fertility a threat? I mean, I look at South Korea, I look at Japan — I think of that certainly as a significant problem for those societies. I mean, within 50 or 60 years, their population level will convulse downwards.

JENNIFER SCIUBBA: If we zoom out on the whole and we look at how globally fertility rates have fallen from way high, six, seven children per woman, down to now two, this is a positive story. It’s something that we worked for, for decades.

EZRA KLEIN: I struggle a little bit with this question of pessimism and fertility. And so I’d like to open it up a little bit. I hear a lot from people who say they don’t want to have children because of climate change or because the world is chaos, and it’s been terrible here, and how can you bring a child into this?

JENNIFER SCIUBBA: Well, a little caveat to that — a lot of these places that are war torn, they don’t have contraceptives, and they are not able to actually make those choices. They don’t have full choice about their reproduction. I think it does come back to this idea about choice. And I will also say that, I mean, I’m in the same bubble you’re in. I spent 15 years in academia, for example. It’s a certain set of people who just might justify their choices based on that.

EZRA KLEIN: One other question that tracks this wealth issue is that a lot of people who are doing very well by global standards, maybe not the richest one percent of Americans or anything, but one thing you’ll hear is that it’s extraordinarily expensive to have children. And I have two children, and I’m here to confirm that it is extraordinarily expensive to have children. But of course, the people having more children are poorer.

JENNIFER SCIUBBA: I think that is a huge factor in why people have fewer children in the U.S. It obviously isn’t just money because we all have more money now than we did. We’re doing better. And so, you can’t just nail it down to say it’s expensive. It really is about this intensity. And some of that intensity is money. I’ve got friends with kids on travel baseball teams — oh, my goodness — a lot of money and a lot of time.

EZRA KLEIN: I just pray my kids don’t play club sports. Like, that’s the only thing I truly want as a parent, for them to not be very good at sports.

JENNIFER SCIUBBA: Yes, mine inherited my lack of ability, so I am winning. Yes, they’re like, can we go to the library? I’m like, you bet, sweetpea. Let’s go. Because, yes, it is just that. And one of my friends, the same one, she’s a stay-at-home mom, and she says, how do people do it if they both work? And the answer is, of course, you either have some prescriptions for some anxiety medication thtat you both pop in the morning, you try to get some help, but it’s hard to get help. People don’t live near parents who can help them, et cetera. It is just such tremendously intensive parenting. And that is the culture now.

EZRA KLEIN: I think a lot about this particular question because I’m so caught on it. Because on the one hand, I get the all joy, no fun theory here. And I don’t find it to be true exactly. I find there to be a lot of fun in it, but I’m also somebody with a pretty flexible job. I work a lot, but I have a fair amount of control over those hours. And I’m somebody with enough money to fill in some of the gaps that we need to fill in. So, we can go out occasionally, that kind of thing.

JENNIFER SCIUBBA: Yes, and we have some data on this. The one that always strikes me is that a working mother today spends more time with her child than a stay-at-home mom would have a few decades ago. We’re spending more time with our kids on average. So I absolutely think that’s the case. And I do think it matters.

EZRA KLEIN: Something that has come up a few times here is simply that women work now. And nobody wants to go back on that, or at least, I don’t want to go back on that. But how much is that just an explanatory factor, that this idea that you’re going to have high fertility in societies where you have dual income, full-time working parents, but also there’s nobody else to take care of the kids, that that just doesn’t fit. I mean, you can say whatever you want. You can do whatever you want. You can have your tax incentives, whatever. But if you’ve got two parents working, it’s just pretty tough, particularly if they’re not making millions of dollars at their jobs.

JENNIFER SCIUBBA: And it’s extra tough when you don’t have a community that supports you. And I think that may be one of the biggest differences now, is that if I think about — I work a highly flexible job. My husband works a less flexible job. So we have a two-income family. But anything I need for support, I’m basically hiring out. I mean, there’s spreadsheets for if I have a work trip. OK, this one’s coming on this day. This one can’t drive. So this one has to do this, that, and the other.

EZRA KLEIN: And I wonder, too, not just about the parents, but the other kids. I mean, I didn’t grow up in the long, long days ago. It still feels fairly recent to me. But I did grow up at a time — I grew up in suburban California. There are kids in almost every house on our block, and they all played outside. And they all just kind of ran around as a pack. And there were younger ones and older ones and everybody played kickball on the garages.

JENNIFER SCIUBBA: Yes, and I do think that makes a difference. I really do. My husband grew up in upstate New York, and he talks all the time about how he and his friends, guys in the neighborhood who were his same age in school and some a little bit older, would get on their bikes, they’d go into the woods, they’d be gone all day long, and nobody thought anything about it. And if one of our sons wants to go over to his friend’s house and he wants to ride his bike, we’re terrified to let him.

Now, part of this is where I live. Statistically, maybe you should be a little bit terrified to let him go, but probably don’t need to be quite as terrified as I am now. But there’s a sense that what if something happened? I would never forgive myself. What will other parents think if I just let my child go out because — and cross a major road. It really is a different intensity to parenting.

I did not grow up in a neighborhood. I grew up in the countryside, and I grew up as an only child. But I was completely independent, and my mom wasn’t saying, OK, you have now played with that litter of puppies for too long. Perhaps you should come inside and eat a snack, or just really micromanaging my life there. And I totally am doing this to my kids. I try not to. I get that I shouldn’t.

EZRA KLEIN: Yeah, to add numbers to that, I think the United States, you mentioned earlier, the fertility rate is about 1.6 — any of these surveys showing that Americans would like to have, on average, 2.7 kids. So, there’s this question of people who don’t want to have kids that gets a lot of attention, but there’s also this question of people who would like to have more children than they do.

JENNIFER SCIUBBA: Yes, and there’s all kinds of little things about this. And we did have two. And we talked about if you had a third, where does the car seat go? We would have to get different cars to be able to fit a third car seat because our kids were close together. I have an 11-year-old son who is not a small guy. He’s a tall guy, 90th percentile. He’s still sitting in the back seat. He’s not supposed to sit in the front seat yet of the car. And that means that only one of you can have a friend come play today if we’re going to drive you anywhere.

EZRA KLEIN: Is that a way that low fertility rates end up feeding on themselves? I lived in San Francisco, which is notoriously a quite low fertility rate major American city. And you could just feel it. You could just feel that there was not infrastructure, really, for kids. I mean, there were some playgrounds, but nothing opened early. But kids get up early. And it’s all these little things that just make it a little bit harder.

JENNIFER SCIUBBA: And I think what’s remarkable about this is that there’s such a divide between rhetoric and action on this. So, in the U.S., the conversation is starting to trend toward, OK, we are a low fertility society. Uh-oh, how do we change that? That’s the rhetoric, but the question we need to ask about the action, then, is, are we really a society that values children and families? And I think in a lot of cases, the answer really is no.

EZRA KLEIN: I was going to ask you about religion as an independent variable here. How much is religion a force that is capable of changing fertility rates? And on the flip side of that, how much is secularization a driver of pushing them down? Are there, in fact, any secular societies or large secular groups that have high fertility rates?

JENNIFER SCIUBBA: Well, I think generally speaking, religiosity matters. It’s about how religious are you, which we think about, how often do you go to services or pray, et cetera. For example, Mormons in the United States have a higher total fertility rate than those who are not religious at all, have low degrees of religiosity. And we tend to see that around the world. Secularism? Yeah, we do see lower rates there. And since we know that religiosity itself is declining, we would expect that to be a pressure towards lower.

EZRA KLEIN: Behind both of those worldviews is not just values, although values are probably there, but also an instrumental sense of what will happen in different scenarios. So the overpopulation folks, they’re worried about the human load on the planet. More humans is, to a first approximation, more carbon dioxide, more material usage, more humans taking up habitat, eating livestock, or raised on arable land, et cetera, et cetera.

Then, there’s the other side, right, the people on the right. And there are people who just believe children are an intrinsic good, that either it is a religious duty or just a beautiful thing, right? More souls in the world, more human beings who can have important, meaningful human experiences. But there’s also a view that sharp demographic decline is a catastrophe from a power and social stability perspective.

JENNIFER SCIUBBA: I think that arguments like that suffer from a significant failure of imagination because what they’re basically saying is that you either grow infinitely, or you collapse. And there is nothing in between. And I think there’s a lot in between. We just love to be alarmist about population. We’re alarmist about it being too high in the ’60s. We’re alarmist about it being too low today.

EZRA KLEIN: One of the other — I don’t know whether to call this a concern or prediction — maybe both — is that you’re just going to see a huge shift in world power as population rates change. So, places with more population are, over time, going to become more powerful. Places with less are going to become less powerful. I think certainly at extreme levels, that is true, right? South Korea is falling by half generation after generation, or more than half. I do think it is going to see its power and sway reduced, and I think it is going to be in more danger from neighbors.

JENNIFER SCIUBBA: I think it’s a very different answer depending on if we’re at the global level or a subnational level. So, if we’re at the global level, does population equal power? Nah. You know why? Because the rules of the game are already written. So, I tend to be an institutionalist in political science terms, meaning I think a lot about the power of the structures, the power of the rules of the game.

EZRA KLEIN: You said at the beginning here, look, there are all these facts, and then there’s what you do with the facts. What do you do with them? We’ve talked a bit about the sort of overpopulation take. We’ve talked a bit about the decline of state. What is your orientation? When you look at societies at 1.5, 1.6 — they seemed to be dropping a little bit lower over time — how do you think about it, and how do you think about where they should be thinking about going from here?

JENNIFER SCIUBBA: I’m trying to strike a balance between showing how important it is to always view demographics, but not so important that you’re willing to take away people’s rights or focus solely on that number, those population numbers, to the extent that you forget to deal with the people who are there.

EZRA KLEIN: I think that is a good place to end. And always our final question, what are three books you’d recommend to the audience?

JENNIFER SCIUBBA: So I have three books that I love for different reasons. So I love “Extra Life” by Steven Johnson. It’s zooming out to say, isn’t it remarkable that we have basically added an extra life because of how much we have improved health and life expectancy?

EZRA KLEIN: Jennifer Sciubba, thank you very much.

EZRA KLEIN: This episode of “The Ezra Klein Show” is produced by Rollin Hu. Fact-checking by Michelle Harris with Kate Sinclair and Mary Marge Locker. Mixing by Isaac Jones and Efim Shapiro. Our senior editor is Claire Gordon. The show’s production team also includes Annie Galvin, Jeff Geld and Kristin Lin. We have original music by Isaac Jones. Audience strategy by Kristina Samulewski and Shannon Busta. The executive producer of New York Times Opinion Audio is Annie-Rose Strasser. And special thanks to Sonia Herrero.

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