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How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

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).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

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.

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.

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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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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How To Write a Strong Research Hypothesis

Are you looking to take your research project to the next level? Have you heard of the power of a hypothesis but need to figure out how to formulate one that will unlock potential discoveries? We can help!

So get ready; it's time to dive into unlocking the power of research! This blog post will explore what makes a well-crafted and powerful hypothesis - from identifying a research question to developing supporting evidence.

By learning how to craft a compelling hypothesis, you'll have more tremendous success in every step of your research project.

What are hypotheses, and why are they important?

A hypothesis is an educated guess or a proposition based on limited evidence as a starting point for further investigation. It provides a framework for research and allows researchers to refine their ideas, collect data, and draw conclusions. Hypotheses are essential to the process because they will enable us to organize our thoughts and test theories properly.

Hypotheses are used in many fields , from medicine to psychology to economics. In each area, developing hypotheses based on observations enable researchers to make predictions about their data and guide them toward finding meaningful results.

For example, in medicine, hypotheses can be used to predict which treatments will be most effective for particular conditions or which drugs may have adverse effects when taken together. This allows doctors to make better decisions when caring for patients.

In psychology, hypotheses are often used in experiments to determine whether certain variables influence behavior or mental processes. By testing different combinations of variables, psychologists can identify patterns and understand why people behave the way they do.

In economics, hypotheses provide economists with a framework for analyzing the relationship between economic variables such as wages and consumer spending habits. By understanding these relationships, economists can better understand how economic forces affect the economy.

Overall, hypotheses play an essential role in helping scientists develop new ideas and draw meaningful conclusions from the collected data. Without taking the step to create hypotheses, it would be difficult for researchers to make sense of the vast amounts of information available today and use it effectively in their investigations.

How to determine an effective research question to form your hypothesis

When conducting research, having a compelling research question is critical . Properly formulating this question will allow the researcher to develop their hypothesis. A research question provides a clear and focused goal for your research study and also gives direction on how to get there. A compelling research question should be specific, answerable in the context of your field of study, significant, novel (not already answered by previous studies), and timely – that is, relevant to current events or trends.

Before determining the best research question, you must first understand your topic. Think about the area of knowledge that interests you most and narrow it down to a single theme or concept within this topic. Focus on what interests you most within this theme, and make sure there is room for further exploration and analysis. Once you have chosen a specific topic and narrowed down your focus, you can begin formulating questions related to your project.

To ensure relevance and impact to your field of study, choose questions that address essential issues in the literature or suggest solutions to existing problems. Avoid overly broad topics with unclear objectives; instead, opt for focused questions to enable targeted data collection and analysis with concrete results.

Additionally, consider time frames when formulating questions. If the issue has been discussed extensively in the past but has not been revisited recently, then it's likely not worthy of a new investigation.

Once you have developed some potential questions related to your topic, review them carefully and decide which question best captures the essence of what you want to learn through researching this topic.

Ask yourself:

• Is this question answerable?
• Does it fit within my field of study?
• Is it significant enough?
• Would its findings be novel?

If so, then congratulations! You have identified a compelling research question.

Tips for crafting a well-crafted hypothesis

Once you have formulated the official research question, you may develop the formal hypothesis. When composing a hypothesis, it's essential to think carefully about the question you are trying to answer.

A solid hypothesis should be testable, meaning that it can be verified or disproved through research. It should also be specific and focused on one issue at a time. Here are some tips for crafting a well-crafted hypothesis:

• Consider the goal of your research: Think about what it is that you want to learn or determine from your experiment and make sure that your hypothesis reflects this goal.
• Create an educated guess as to why something is happening: Your hypothesis should explain why something is occurring based on what evidence you already have and direct further investigation into the matter. For example, if you hypothesize that increased carbon dioxide levels in the atmosphere will lead to global warming, your research should focus on examining this relationship further.
• Define any variables or parameters involved in the experiment: This includes things like temperature or chemical composition that could potentially affect the outcome of any experiments done in pursuit of testing your hypothesis.
• Use clear and precise language: Make sure your hypothesis is written with clear and precise language so that anyone reading it can understand exactly what you are attempting to investigate or explain. Avoid complex words and keep sentences short whenever possible.

Following these simple tips will help ensure that your hypothesis is well-crafted and ready for testing!

Examples of evidence that can support your hypothesis

When it comes to developing a hypothesis, supporting evidence is essential for making sure it holds up. This evidence helps strengthen the argument that is being driven by providing facts and logical reasoning that support the hypothesis.

Examples of evidence that can be used to back up a hypothesis include using data from experiments, case studies, and other research projects. Data from experiments can provide insight into how certain variables interact to form a particular outcome.

Case studies may offer greater depth in understanding a specific phenomenon's cause and effect; research projects may yield results that confirm or refute existing theories on a subject.

In addition to these traditional forms of evidence, personal experiences or observations can also help to support a hypothesis. For example, if someone's daily commute has been consistently faster since they changed routes, they could use their personal experience to argue that making this change resulted in shorter commutes.

Similarly, suppose someone has witnessed how two variables consistently coincide (i.e., when one goes up, another goes down). In that case, this could be used to support the notion that there is some correlation between these two aspects.

Overall, evidence to support your hypothesis is crucial for ensuring its validity and credibility. While conducting experiments or researching may seem like time-consuming processes, having solid supporting evidence will make it much easier to defend your ideas convincingly when challenged.

Therefore, it is crucial to take the time necessary to gather credible sources of information to provide the most substantial possible backing for your hypotheses.

Understanding the potential of hypotheses and how they can help your research project progress

The power of research lies in the ability to develop and test hypotheses. A hypothesis is a statement or an idea that can be tested to determine its validity.

Essentially, it is a form of educated guesswork that helps researchers form conclusions about their data. By developing a hypothesis for a research project, you are effectively setting up the framework for further exploration.

When developing a hypothesis, you must consider both the expected outcomes and possible alternative explanations. This will help you focus on testing the possible results without getting sidetracked by irrelevant information. Once you have established a concrete hypothesis, it can then be used as a basis for further research and experimentation.

The process of testing hypotheses is an integral part of the scientific method and can help researchers build confidence in their findings and conclusions. Through careful observation and experimentation, researchers can compare their results against what they initially hypothesized, allowing them to draw more accurate conclusions about their data. As such, hypotheses play an essential role in helping researchers connect the dots between different pieces of evidence and form meaningful conclusions.

Overall, understanding how hypotheses can be used in research projects can be immensely beneficial in helping progress towards reaching meaningful insights from their data. By setting up expectations ahead of time and then testing them against real-world conditions, researchers can gain valuable insights that could potentially change the way we understand our world – now that's something worth exploring!

Final thoughts

A hypothesis is a proposed explanation for an observable phenomenon. It's important to note that hypotheses are not the same thing as theories–a theory is a much broader and well-established frame of reference that explains multiple phenomena.

Generally, scientists form a research question and then narrow it down to a testable hypothesis. After making observations and conducting experiments to gather data, researchers can use evidence to support or reject the hypothesis.

By following these steps to formulate a solid hypothesis, you will be on your way to developing a successful research project. Happy researching!

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How to Write a Hypothesis: A Step-by-Step Guide

Introduction

An overview of the research hypothesis, different types of hypotheses, variables in a hypothesis, how to formulate an effective research hypothesis, designing a study around your hypothesis.

The scientific method can derive and test predictions as hypotheses. Empirical research can then provide support (or lack thereof) for the hypotheses. Even failure to find support for a hypothesis still represents a valuable contribution to scientific knowledge. Let's look more closely at the idea of the hypothesis and the role it plays in research.

As much as the term exists in everyday language, there is a detailed development that informs the word "hypothesis" when applied to research. A good research hypothesis is informed by prior research and guides research design and data analysis , so it is important to understand how a hypothesis is defined and understood by researchers.

What is the simple definition of a hypothesis?

A hypothesis is a testable prediction about an outcome between two or more variables. It functions as a navigational tool in the research process, directing what you aim to predict and how.

What is the hypothesis for in research?

In research, a hypothesis serves as the cornerstone for your empirical study. It not only lays out what you aim to investigate but also provides a structured approach for your data collection and analysis.

Essentially, it bridges the gap between the theoretical and the empirical, guiding your investigation throughout its course.

What is an example of a hypothesis?

If you are studying the relationship between physical exercise and mental health, a suitable hypothesis could be: "Regular physical exercise leads to improved mental well-being among adults."

This statement constitutes a specific and testable hypothesis that directly relates to the variables you are investigating.

What makes a good hypothesis?

A good hypothesis possesses several key characteristics. Firstly, it must be testable, allowing you to analyze data through empirical means, such as observation or experimentation, to assess if there is significant support for the hypothesis. Secondly, a hypothesis should be specific and unambiguous, giving a clear understanding of the expected relationship between variables. Lastly, it should be grounded in existing research or theoretical frameworks , ensuring its relevance and applicability.

Understanding the types of hypotheses can greatly enhance how you construct and work with hypotheses. While all hypotheses serve the essential function of guiding your study, there are varying purposes among the types of hypotheses. In addition, all hypotheses stand in contrast to the null hypothesis, or the assumption that there is no significant relationship between the variables.

Here, we explore various kinds of hypotheses to provide you with the tools needed to craft effective hypotheses for your specific research needs. Bear in mind that many of these hypothesis types may overlap with one another, and the specific type that is typically used will likely depend on the area of research and methodology you are following.

Null hypothesis

The null hypothesis is a statement that there is no effect or relationship between the variables being studied. In statistical terms, it serves as the default assumption that any observed differences are due to random chance.

For example, if you're studying the effect of a drug on blood pressure, the null hypothesis might state that the drug has no effect.

Alternative hypothesis

Contrary to the null hypothesis, the alternative hypothesis suggests that there is a significant relationship or effect between variables.

Using the drug example, the alternative hypothesis would posit that the drug does indeed affect blood pressure. This is what researchers aim to prove.

Simple hypothesis

A simple hypothesis makes a prediction about the relationship between two variables, and only two variables.

For example, "Increased study time results in better exam scores." Here, "study time" and "exam scores" are the only variables involved.

Complex hypothesis

A complex hypothesis, as the name suggests, involves more than two variables. For instance, "Increased study time and access to resources result in better exam scores." Here, "study time," "access to resources," and "exam scores" are all variables.

This hypothesis refers to multiple potential mediating variables. Other hypotheses could also include predictions about variables that moderate the relationship between the independent variable and dependent variable .

Directional hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. For example, "Eating more fruits and vegetables leads to a decrease in heart disease."

Here, the direction of heart disease is explicitly predicted to decrease, due to effects from eating more fruits and vegetables. All hypotheses typically specify the expected direction of the relationship between the independent and dependent variable, such that researchers can test if this prediction holds in their data analysis .

Statistical hypothesis

A statistical hypothesis is one that is testable through statistical methods, providing a numerical value that can be analyzed. This is commonly seen in quantitative research .

For example, "There is a statistically significant difference in test scores between students who study for one hour and those who study for two."

Empirical hypothesis

An empirical hypothesis is derived from observations and is tested through empirical methods, often through experimentation or survey data . Empirical hypotheses may also be assessed with statistical analyses.

For example, "Regular exercise is correlated with a lower incidence of depression," could be tested through surveys that measure exercise frequency and depression levels.

Causal hypothesis

A causal hypothesis proposes that one variable causes a change in another. This type of hypothesis is often tested through controlled experiments.

For example, "Smoking causes lung cancer," assumes a direct causal relationship.

Associative hypothesis

Unlike causal hypotheses, associative hypotheses suggest a relationship between variables but do not imply causation.

For instance, "People who smoke are more likely to get lung cancer," notes an association but doesn't claim that smoking causes lung cancer directly.

Relational hypothesis

A relational hypothesis explores the relationship between two or more variables but doesn't specify the nature of the relationship.

For example, "There is a relationship between diet and heart health," leaves the nature of the relationship (causal, associative, etc.) open to interpretation.

Logical hypothesis

A logical hypothesis is based on sound reasoning and logical principles. It's often used in theoretical research to explore abstract concepts, rather than being based on empirical data.

For example, "If all men are mortal and Socrates is a man, then Socrates is mortal," employs logical reasoning to make its point.

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In any research hypothesis, variables play a critical role. These are the elements or factors that the researcher manipulates, controls, or measures. Understanding variables is essential for crafting a clear, testable hypothesis and for the stages of research that follow, such as data collection and analysis.

In the realm of hypotheses, there are generally two types of variables to consider: independent and dependent. Independent variables are what you, as the researcher, manipulate or change in your study. It's considered the cause in the relationship you're investigating. For instance, in a study examining the impact of sleep duration on academic performance, the independent variable would be the amount of sleep participants get.

Conversely, the dependent variable is the outcome you measure to gauge the effect of your manipulation. It's the effect in the cause-and-effect relationship. The dependent variable thus refers to the main outcome of interest in your study. In the same sleep study example, the academic performance, perhaps measured by exam scores or GPA, would be the dependent variable.

Beyond these two primary types, you might also encounter control variables. These are variables that could potentially influence the outcome and are therefore kept constant to isolate the relationship between the independent and dependent variables . For example, in the sleep and academic performance study, control variables could include age, diet, or even the subject of study.

By clearly identifying and understanding the roles of these variables in your hypothesis, you set the stage for a methodologically sound research project. It helps you develop focused research questions, design appropriate experiments or observations, and carry out meaningful data analysis . It's a step that lays the groundwork for the success of your entire study.

Crafting a strong, testable hypothesis is crucial for the success of any research project. It sets the stage for everything from your study design to data collection and analysis . Below are some key considerations to keep in mind when formulating your hypothesis:

• Be specific : A vague hypothesis can lead to ambiguous results and interpretations . Clearly define your variables and the expected relationship between them.
• Ensure testability : A good hypothesis should be testable through empirical means, whether by observation , experimentation, or other forms of data analysis.
• Ground in literature : Before creating your hypothesis, consult existing research and theories. This not only helps you identify gaps in current knowledge but also gives you valuable context and credibility for crafting your hypothesis.
• Use simple language : While your hypothesis should be conceptually sound, it doesn't have to be complicated. Aim for clarity and simplicity in your wording.
• State direction, if applicable : If your hypothesis involves a directional outcome (e.g., "increase" or "decrease"), make sure to specify this. You also need to think about how you will measure whether or not the outcome moved in the direction you predicted.
• Keep it focused : One of the common pitfalls in hypothesis formulation is trying to answer too many questions at once. Keep your hypothesis focused on a specific issue or relationship.
• Account for control variables : Identify any variables that could potentially impact the outcome and consider how you will control for them in your study.
• Be ethical : Make sure your hypothesis and the methods for testing it comply with ethical standards , particularly if your research involves human or animal subjects.

Designing your study involves multiple key phases that help ensure the rigor and validity of your research. Here we discuss these crucial components in more detail.

Literature review

Starting with a comprehensive literature review is essential. This step allows you to understand the existing body of knowledge related to your hypothesis and helps you identify gaps that your research could fill. Your research should aim to contribute some novel understanding to existing literature, and your hypotheses can reflect this. A literature review also provides valuable insights into how similar research projects were executed, thereby helping you fine-tune your own approach.

Research methods

Choosing the right research methods is critical. Whether it's a survey, an experiment, or observational study, the methodology should be the most appropriate for testing your hypothesis. Your choice of methods will also depend on whether your research is quantitative, qualitative, or mixed-methods. Make sure the chosen methods align well with the variables you are studying and the type of data you need.

Preliminary research

Before diving into a full-scale study, it’s often beneficial to conduct preliminary research or a pilot study . This allows you to test your research methods on a smaller scale, refine your tools, and identify any potential issues. For instance, a pilot survey can help you determine if your questions are clear and if the survey effectively captures the data you need. This step can save you both time and resources in the long run.

Data analysis

Finally, planning your data analysis in advance is crucial for a successful study. Decide which statistical or analytical tools are most suited for your data type and research questions . For quantitative research, you might opt for t-tests, ANOVA, or regression analyses. For qualitative research , thematic analysis or grounded theory may be more appropriate. This phase is integral for interpreting your results and drawing meaningful conclusions in relation to your research question.

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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.

• Collecting Data

Frequently Asked Questions

A hypothesis is a tentative statement about the relationship between two or more  variables. It is a specific, testable prediction about what you expect to happen in a study.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore a number of factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk wisdom that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis.   In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in a number of different ways. One of the basic principles of any type of scientific research is that the results must be replicable.   By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. How would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests that there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative sample of the population and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• Complex hypothesis: "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
• "There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

• "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
• "Adults will perform better on a memory task than children." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when it would be impossible or difficult to  conduct an experiment . These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can then be used to look at how the variables are related. This type of research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

A Word From Verywell

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Some examples of how to write a hypothesis include:

• "Staying up late will lead to worse test performance the next day."
• "People who consume one apple each day will visit the doctor fewer times each year."
• "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

• The research question
• The independent variable (IV)
• The dependent variable (DV)
• The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

This is awesome.Wow.

It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

Explained well and in simple terms. Quick read! Thank you

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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

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The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

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.

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).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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A concise guide to reproducible research using secondary data

Chapter 2 formulating a hypothesis.

“There is no single best way to develop a research idea.” ( Pischke 2012 )

2.1 How do you develop a research question and formulate a hypothesis?

You decide to undertake a scientific project. Where do you start? First, you need to find a research question that interests you and formulate a hypothesis. We will introduce some key terminology, steps you can take, and examples how to develop research questions. Note that .

What if someone assigns a topic to me? For students attending undergraduate and graduate courses that often pick topics from a list, all of these steps are equally important and necessary. You still need to formulate a research question and a hypothesis. And it is important to clarify the relevance of your topic for yourself.

When thinking about a research question, you need to identify a topic that is:

• Relevant , important in the world and interesting to you as a researcher: Does working on the topic excites you? You will spend many hours thinking about it and working on it. Therefore, it should be interesting and engaging enough for you to motivate your continued work on this topic.
• Specific : not too broad and not too narrow
• Feasible to research within a given time frame: Is it possible to answer the research question based on your time budget, data and additional resources.

How do you find a topic or develop a feasible research idea in the first place? Finding an idea is not difficult, the critical part is to find a good idea. How do you do that? There is no one specific way how one gets an idea, rather there is a myriad of ways how people come up with potential ideas (for example, as stated by Varian ( 2016 ) ).

You can find inspiration by

• Looking at insights from the world around you: your own life and experiences, observe the behavior of people around you
• Talking to people around you, experts, other students, family members
• Talking to individuals outside your field (non-economists)
• Talking to professionals working in the area you are interested in (you may use social media and professional platforms like LinkedIN or Twitter to make contact)
• Reading journal articles from other non-economic social sciences and the medical literature
• What are the issues being discussed?
• How do these issues affect people’s lives?

In addition you could

• Go to virtual and in-person seminars, for example, the Essen Health Economics Seminar
• Look at abstracts of scientific articles and working papers
• Look at the literature in a specific field you are interested in, for example, screening complete issues of journals or editorials about certain research advancements. By reading this literature you might come up with the idea on how to extend and refine previous research.

Once you identified a research question that is of interest to you, you need to define a hypothesis.

2.2 What is a hypothesis?

A hypothesis is a statement that introduces your research question and suggests the results you might find. It is an educated guess. You start by posing an economic question and formulate a hypothesis about this question. Then you test it with your data and empirical analysis and either accept or reject the hypothesis. It constitutes the main basis of your scientific investigation and you should be careful when creating it.

2.2.1 Develop a hypothesis

Before you formulate your hypothesis, read up on the topic of interest. This should provide you with sufficient information to narrow down your research question. Once you find your question you need to develop a hypothesis, which contains a statement of your expectations regarding your research question’s results. You propose to prove your hypothesis with your research by testing the relationship between two variables of interest. Thus, a hypothesis should be testable with the data at hand. There are two types of hypotheses: alternative or null. Null states that there is no effect. Alternative states that there is an effect.

There is an alternative view on this that suggests one should not look at the literature too early on in the idea-generating process to not be influenced and shaped by someone else’s ideas ( Varian 2016 ) . According to this view you can spend some time (i.e. a few weeks) trying to develop your own original idea. Even if you end up with an idea that has already been pursued by someone else, this will still provide you with good practice in developing publishable ideas. After you have developed an idea and made sure that it was not yet investigated in the literature, you can start conducting a systematic literature review. By doing this, you can find some other interesting insights from the work of others that you can synthesize in your own work to produce something novel and original.

2.2.2 Identify relevant literature

For your research project you will need to identify and collect previous relevant literature. It should involve a thorough search of the keywords in relevant databases and journals. Place emphasis on articles from high-ranking journals with significant numbers of citations. This will give you an indication of the most influential and important work in the field. Once you identify and collect the relevant literature for your topic, you will need to critically synthesize it in your literature review.

When you perform your literature review, consider theories that may inform your research question. For example, when studying physician behavior you may consider principal-agent theory.

2.2.3 Research question or literature review: the chicken or the egg problem?

Whether you start reading the literature first or by developing an idea may depend on your level (graduate student, early career researcher) and other goals. However, thinking freely about what you like to investigate first may help to critically develop a feasible and interesting research question.

We highlight an example how to start with investigating the real world and subsequently posing a research question ( “How to Write a Strong Hypothesis Steps and Examples ” 2019 ; “Developing Strong Research Questions Criteria and Examples ” 2019 ; Schilbach 2019 ) . For example, based on your observation you notice that people spend extensive amount of time looking at their smartphones. Maybe even you yourself engage in the same behavior. In addition, you read a BBC News article Social media damages teenagers’ mental health, report says .

(#fig:social_media)Social media and mental health

Source: BBC

You decide to translate this article and your observations into a research question : How does social media use affect mental health? Before you formulate your hypothesis, read up on the topic of interest. Read economic, medical and other social science literature on the topic. There is likely to be a vast amount of literature from non-economic fields that are doing research on your topic of interest, for example, psychology or neuroscience. Familiarize yourself with it and master it. Do not get distracted by different scientific methodologies and techniques that might seem not up-to-par to the economic studies (small sample sizes, endogeneity, uncovering association rather than causation, etc.), but rather focus on suggestions of potential mechanisms.

A hypothesis is then your research question distilled into a one sentence statement, which presents your expectations regarding the results. You propose to prove your hypothesis by testing the relationship between two variables of interest with the data at hand. There are two types of hypotheses: alternative or null. The null hypothesis states that there is no effect. The alternative hypothesis states that there is an effect.

A hypothesis related to the above-stated research question could be: The increased use of social media among teenagers leads to (is associated with) worse mental health outcomes, i.e. increased incidence of depression, eating disorders, worse well-being and lower self-esteem. It suggests a direction of a relationship that you expect to find that is guided by your observations and existing evidence. It is testable with scientific research methods by using statistical analysis of the relevant data.

Your hypothesis suggests a relationship between two variables: social media use (your independent variable \(X\) ) and mental health (dependent variable \(Y\) ). It could be framed in terms of correlation (is associated with) or causation (leads to). This should be reflected in the choice of scientific investigation you decide to undertake.

The null hypothesis is: There is no relationship between social media use among teenagers and their mental health .

2.3 Resources box

2.3.1 how to develop strong research questions.

• The form of the research process
• Varian, H. R. (2016). How to build an economic model in your spare time. The American Economist, 61(1), 81-90.

2.3.2 Identify relevant literature from major general interest and field literature

To identify the relevant literature you can

• use academic search engines such as Google Scholar, Web of Science, EconLit, PubMed.
• search working paper series such as the National Bureau of Economic Research , NetEc or IZA
• search more general resource sites such as Resources for Economists
• go to the library/use library database

2.3.3 Assess the quality of a journal article

Several rankings may help to assess the quality of research you consider

• Journals of general interest and by field in economics and management - For German-speaking countries, consider the VWL / BWL Handelsblatt Ranking for economics and management - The German Association of Management Scholars provides an expert-based ranking VHB JourQual 3.0, Teilranking Management im Gesundheitswesen - Web of Science Impact Factors - Scimago
• Health Economics, Health Services and Health Care Managment Research: Health Economics Journals List
• Be aware that like in any other domain there are predatory publishing practices .

Use tools to investigate how a journal article is connected to other works

• Citationgecko
• Connected papers
• scite_ – a tool to get a first impression whether a study is disputed or academic consensus

2.3.4 Organize your literature

• Zotero (free of charge)
• Mendeley (free of charge)
• EndNote (potentially free of charge via your university)
• Citavi (potentially free of charge via your university)
• BibTEX if you work with TEX
• Excel spread sheet

2.4 Checklist to get started with formulating your hypothesis

• Find an interesting and relevant research topic, if not assigned
• Try to suck up all information you can easily obtain from various sources within and outside academic literature
• Formulate one compelling research question
• Find the best available empirical and theoretical evidence that is related to your research question
• Formulate a hypothesis
• Check whether data are available for analysis
• Challenge your idea with your fellows or senior researchers

2.5 Example: Hellerstein ( 1998 )

As an illustration of the research process of formulating a hypothesis, designing a study, running a study, collecting and analyzing the data and, finally, reporting the study, we provide an example by replicating Judith K. Hellerstein’s paper “The Importance of the Physician in the Generic versus Trade-Name Prescription Decision” that was published in 1998 in the RAND Journal of Economics.

Hellerstein’s 1998 paper has impacted discussion about behavioral factors of physician decisions and pharmaceutical markets over two decades. The study received 448 citations on Google Scholar since 1998 by 27/03/2022, including recent mentions in top field journals such as Journal of Public Economics (2021) , Journal of Health Economics (2019) , and Health Economics (2019) .

Figure 2.1: Connected graph of Hellerstein ( 1998 ) , February 2022

Figure 2.1 shows a connected graph of prior and derivative works related to the study.

The work has impacted the literature researching the role of physician behavior and its influence on access, adoption and diffusion of health services, moral hazard and incentives in prescription and treatment decisions and the influence of different payment schemes, and a vast body of literature studying the pharmaceutical market.

The research that has been influenced by Hellerstein includes evidence on:

• generic drug entries and market efficiency
• the effectiveness of pharmaceutical promotion
• the effectiveness of price regulations
• the role of patents and dynamics of market segmentation

At the end of each chapter, we demonstrate insights into this study that we replicate.

2.5.1 Context of the study - escalating health expenditures

In the United States, the total prescription drug expenditure in 2020 marked about 358.7 billion US Dollars ( Statista n.d. ) . The prescription of generic drugs in comparison to more expensive brand-name versions is an option in reducing the total health care expenditure. Generic drugs are bioequivalent in the active ingredients and can serve as a channel to contain prescription expenditure ( Kesselheim 2008 ) as generic drugs are between 20 and 90% cheaper than their trade-name alternatives ( Dunne et al. 2013 ) .

2.5.2 Research question - How does a patient’s insurance status influence the physician’s choice between generic compared to brand-name drugs?

Physicians are faced with a multitude of medication options, including the choice between generic and trade-name drugs. Physicians ideally act as agents for their patients to identify the best available treatment option based on their needs. Choosing the best treatment entails cost of coordination and cognition. The prescription of generic drugs may serve as an example to what extent physicians customize treatments according to patients’ needs with regards to cost. From an economic point of view we may expect that once a generic drug is available, a perfectly rational agent (i.e. physician) would prescribe a generic drug instead of the trade-name version if therapeutically identical ( Dranove 1989 ) . This leads to the following research question: “Do physicians vary their prescription decisions on a patient-by-patient basis or do they systematically prescribe the same version, trade-name or generic, to all patients?” .

The 1998 Hellerstein’s study examines two hypotheses:

• The physician prescribing choice influences the selection of a generic over a brand-name drug
• The patient’s insurance status influences the physician’s choice between generic and brand-name drugs.

For the purpose of this example and in the replication exercise we focus on the second aspect.

2.5.3 Hypothesis

The paper formulates the following hypothesis:

Physicians are more likely to prescribe generics to patients who do not have insurance coverage for prescription pharmaceuticals (moral hazard in insurance)

Hellerstein ( 1998 ) discusses that, based on insurance status, some patients may demand certain care more than others. If, for example, the prescription drug is reimbursed by the patient’s health insurance, this may cause overconsumption. This behavior can potentially differ by the patient’s insurance scheme. A patient that has no insurance and, thus, does not get any reimbursement for prescription drugs, might have a higher incentive to demand cheaper generic drugs ( Danzon and Furukawa 2011 ) than a patient with insurance that covers prescription drugs, either generic or trade-name. Given that the United States have different insurance schemes with varying prescription drug coverage, it is of interest to investigate the role of a patient’s insurance status in the physician’s choice between generic compared to brand-name drugs.

Hellerstein ( 1998 ) considers a patient’s insurance status as a matter of dividing the study population in groups for which the choice between generic and brand-name drugs differs. She suggests that There is a relationship between the prescription of a generic drug and insurance status of a patient. ( Hellerstein 1998 ) .

Providing answers to a research question requires formulating and testing a hypothesis. Based on logic, theory or previous research, a hypothesis proposes an expected relationship within the given data. According to her research question, Hellerstein hypothesizes that: Physicians are more likely to prescribe generics to patients who do not have insurance coverage for prescription pharmaceuticals.

Specifically, she writes “if there is moral hazard in insurance when it comes to physician prescription behavior, there will be differences in the propensity of physicians to prescribe low-cost generic drugs, and these differences will be (partially) a function of the insurance held by the patient. In particular, if moral hazard exists, patients with extensive insurance coverage for prescription drugs (like those on Medicaid in 1989) should receive prescriptions written for generic drugs less frequently than patients with no prescription drug coverage.” ( Hellerstein 1998, 113 )

Based on Hellerstein’s considerations, we expect the effect of the insurance status on whether a patient receives a generic to be different from zero. To obtain a testable null hypothesis, we reformulate this relationship so that we reject the hypothesis if our expectations are correct. This means, if we expect to see an effect of insurance on prescriptions of generics, our null hypothesis is that insurance status has no effect on the outcome (prescription of generic drugs). No moral hazard arises from having obtained insurance.

Crime Analysis for Problem Solvers in 60 Small Steps

Step 20: formulate hypotheses.

Whenever we confront some new and perplexing crime pattern we form hypotheses about its causes, often based on incomplete information. Experience and theory are good sources of hypotheses. You should (1) clearly state your hypotheses, (2) not be wedded to them, and (3) use data to objectively test them. Expect all hypotheses to be altered or discarded once relevant data have been examined because no hypothesis is completely right. For this reason it is often best to test multiple conflicting hypotheses.

A set of hypotheses is a roadmap for analysis. Hypotheses suggest types of data to collect, how this data should be analyzed, and how to interpret analysis results. If you were investigating drinking-related assaults in bars you might begin with the question, "How many bars are problem locations?" Based on the 80-20 rule (Step 18), you would state the hypothesis that some bars will have many fights, but most will have few or none. You would then test this hypothesis by listing the licensed drinking places and counting the number of assault reports at each over the last 12 months.

If your hypothesis was supported, you might ask the question, "What is different about the bars with many fights compared to the bars with few assaults?" The concept of risky facilities (Step 28) would help you form a set of three hypotheses:

• Risky bars have more customers.
• Risky bars have features that attract assaulters.
• Bar staff in risky bars either fail to control behaviors, or provoke fights.

You can test these hypotheses by gathering data on the number of customers at high- and low-risk bars, analyzing the number and rate of assaults per customer, observing the interactions of people at troublesome and trouble-free bars, and interviewing staff and customers.

If your first hypothesis was contradicted by the data, and you found that there was no great difference in numbers of assaults across drinking establishments, then you might ask the question, why are so many bars troublesome? This suggests another hypothesis: It's a perception problem; the city has about as many bar assaults as other comparable cities. This hypothesis suggests that you will need data from comparable cities.

If, after you collected the relevant data, you found that your city has an abnormally high number of problem bars, you might ask the question, "What is common to most bars in the city that produces a large number of assaults?" One hypothesis is that it is the way liquor licenses are dispensed and bars regulated. Another hypothesis is that there is something about the nature of bar customers in your city. Testing each would require you to collect relevant data and assess the validity of the hypothesis.

Notice how the questions and hypotheses structure the analysis. Test results - positive or negative - reveal new, more specific questions. The objective is to start with broad questions and hypotheses and, through a pruning process, come to a set of highly focused questions that point to possible responses.

Hypotheses suggest the type of data to collect. In the bar assaults example, the test of each hypothesis requires specific data. Sometimes the same data can test multiple hypotheses (as is the case with choosing among the three alternative explanations for risky bars). Often a variety of data is required to select among alternative hypotheses (as is the case with the last set of hypotheses). The more specific your hypotheses, the more focused your data collection will be. This is why it is more important to have a clear hypothesis you personally dislike, than an unclear hypothesis you approve of, or worse, no hypothesis at all.

Paralysis by Analysis

The lack of explicit hypotheses can lead to "paralysis by analysis," collecting too much data, conducting too much analysis, and not coming to any useful conclusion.

Hypotheses can help direct the analysis of data. Every clear hypothesis suggests a pattern of data that you should be able to observe, if the hypothesis is correct. In the example above, the hypotheses derived from the concept of risky facilities can be tested using a simple analytical procedure. If a bar is a crime generator, then you should see a high number of assaults, a high number of customers, but a low assault rate (see Step 17). Failure to find this pattern suggests the hypothesis is wrong. So it is important to have a clear idea of what you should observe if your hypothesis is correct, and what you should observe if your hypothesis is wrong (see third column of the table). If you cannot do this, then this is an indicator that your hypothesis may be too vague.

Hypotheses help interpret the analysis results. Let's assume that the analysis of bar fights showed that a few bars had most fights, and observations of the high- and low-risk bars indicated that the security staff of the risky bars provoked fights. This immediately suggests a possible avenue for intervention. In short, the validity of a hypothesis must make a difference. That is, if the hypothesis is true you will take a different decision than if it is false. If you will make the same decision regardless of the test results, then the hypothesis and its test are irrelevant.

In summary, hypotheses are important for guiding analysis. To formulate hypotheses you need to ask important questions, then create simple and direct speculative answers to these questions. These answers are your hypotheses. These speculations must be bold enough that they could be wrong, and there must be a way of showing whether they are right or wrong. If possible, create competing hypotheses.

Hypothesis formation is a useful group exercise, as it allows participants with contrary views to put their perspectives on the table in a way that allows clear and objective tests. In this way, participants contributing invalid hypotheses make substantial contributions to the analysis of the problem. If each hypothesis is linked to a potential solution, the test of these hypotheses simultaneously directs attention to feasible responses and rules out ineffective approaches.

Questions, Hypotheses, and Tests

• Acknowledgements

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

Useful Links

Learn How To Write A Hypothesis For Your Next Research Project!

Undoubtedly, research plays a crucial role in substantiating or refuting our assumptions. These assumptions act as potential answers to our questions. Such assumptions, also known as hypotheses, are considered key aspects of research. In this blog, we delve into the significance of hypotheses. And provide insights on how to write them effectively. So, let’s dive in and explore the art of writing hypotheses together.

Table of Contents

What is a Hypothesis?

A hypothesis is a crucial starting point in scientific research. It is an educated guess about the relationship between two or more variables. In other words, a hypothesis acts as a foundation for a researcher to build their study.

Here are some examples of well-crafted hypotheses:

• Increased exposure to natural sunlight improves sleep quality in adults.

A positive relationship between natural sunlight exposure and sleep quality in adult individuals.

• Playing puzzle games on a regular basis enhances problem-solving abilities in children.

Engaging in frequent puzzle gameplay leads to improved problem-solving skills in children.

• Students and improved learning hecks.

S tudents using online  paper writing service  platforms (as a learning tool for receiving personalized feedback and guidance) will demonstrate improved writing skills. (compared to those who do not utilize such platforms).

• The use of APA format in research papers. 

Using the  APA format  helps students stay organized when writing research papers. Organized students can focus better on their topics and, as a result, produce better quality work.

The Building Blocks of a Hypothesis

To better understand the concept of a hypothesis, let’s break it down into its basic components:

• Variables . A hypothesis involves at least two variables. An independent variable and a dependent variable. The independent variable is the one being changed or manipulated, while the dependent variable is the one being measured or observed.
• Relationship : A hypothesis proposes a relationship or connection between the variables. This could be a cause-and-effect relationship or a correlation between them.
• Testability : A hypothesis should be testable and falsifiable, meaning it can be proven right or wrong through experimentation or observation.

Types of Hypotheses

When learning how to write a hypothesis, it’s essential to understand its main types. These include; alternative hypotheses and null hypotheses. In the following section, we explore both types of hypotheses with examples. 

Alternative Hypothesis (H1)

This kind of hypothesis suggests a relationship or effect between the variables. It is the main focus of the study. The researcher wants to either prove or disprove it. Many research divides this hypothesis into two subsections: 

• Directional 

This type of H1 predicts a specific outcome. Many researchers use this hypothesis to explore the relationship between variables rather than the groups. 

• Non-directional

You can take a guess from the name. This type of H1 does not provide a specific prediction for the research outcome. 

Here are some examples for your better understanding of how to write a hypothesis.

• Consuming caffeine improves cognitive performance.  (This hypothesis predicts that there is a positive relationship between caffeine consumption and cognitive performance.)
• Aerobic exercise leads to reduced blood pressure.  (This hypothesis suggests that engaging in aerobic exercise results in lower blood pressure readings.)
• Exposure to nature reduces stress levels among employees.  (Here, the hypothesis proposes that employees exposed to natural environments will experience decreased stress levels.)
• Listening to classical music while studying increases memory retention.  (This hypothesis speculates that studying with classical music playing in the background boosts students’ ability to retain information.)
• Early literacy intervention improves reading skills in children.  (This hypothesis claims that providing early literacy assistance to children results in enhanced reading abilities.)
• Time management in nursing students. ( Students who use a  nursing research paper writing service  have more time to focus on their studies and can achieve better grades in other subjects. )

Null Hypothesis (H0)

A null hypothesis assumes no relationship or effect between the variables. If the alternative hypothesis is proven to be false, the null hypothesis is considered to be true. Usually a null hypothesis shows no direct correlation between the defined variables. 

Here are some of the examples

• The consumption of herbal tea has no effect on sleep quality.  (This hypothesis assumes that herbal tea consumption does not impact the quality of sleep.)
• The number of hours spent playing video games is unrelated to academic performance.  (Here, the null hypothesis suggests that no relationship exists between video gameplay duration and academic achievement.)
• Implementing flexible work schedules has no influence on employee job satisfaction.  (This hypothesis contends that providing flexible schedules does not affect how satisfied employees are with their jobs.)
• Writing ability of a 7th grader is not affected by reading editorial example. ( There is no relationship between reading an  editorial example  and improving a 7th grader’s writing abilities.) 
• The type of lighting in a room does not affect people’s mood.  (In this null hypothesis, there is no connection between the kind of lighting in a room and the mood of those present.)
• The use of social media during break time does not impact productivity at work.  (This hypothesis proposes that social media usage during breaks has no effect on work productivity.)

As you learn how to write a hypothesis, remember that aiming for clarity, testability, and relevance to your research question is vital. By mastering this skill, you’re well on your way to conducting impactful scientific research. Good luck!

Importance of a Hypothesis in Research

A well-structured hypothesis is a vital part of any research project for several reasons:

• It provides clear direction for the study by setting its focus and purpose.
• It outlines expectations of the research, making it easier to measure results.
• It helps identify any potential limitations in the study, allowing researchers to refine their approach.

In conclusion, a hypothesis plays a fundamental role in the research process. By understanding its concept and constructing a well-thought-out hypothesis, researchers lay the groundwork for a successful, scientifically sound investigation.

How to Write a Hypothesis?

Here are five steps that you can follow to write an effective hypothesis. 

Step 1: Identify Your Research Question

The first step in learning how to compose a hypothesis is to clearly define your research question. This question is the central focus of your study and will help you determine the direction of your hypothesis.

Step 2: Determine the Variables

When exploring how to write a hypothesis, it’s crucial to identify the variables involved in your study. You’ll need at least two variables:

• Independent variable : The factor you manipulate or change in your experiment.
• Dependent variable : The outcome or result you observe or measure, which is influenced by the independent variable.

Step 3: Build the Hypothetical Relationship

In understanding how to compose a hypothesis, constructing the relationship between the variables is key. Based on your research question and variables, predict the expected outcome or connection. This prediction should be specific, testable, and, if possible, expressed in the “If…then” format.

Step 4: Write the Null Hypothesis

When mastering how to write a hypothesis, it’s important to create a null hypothesis as well. The null hypothesis assumes no relationship or effect between the variables, acting as a counterpoint to your primary hypothesis.

Step 5: Review Your Hypothesis

Finally, when learning how to compose a hypothesis, it’s essential to review your hypothesis for clarity, testability, and relevance to your research question. Make any necessary adjustments to ensure it provides a solid basis for your study.

In conclusion, understanding how to write a hypothesis is crucial for conducting successful scientific research. By focusing on your research question and carefully building relationships between variables, you will lay a strong foundation for advancing research and knowledge in your field.

Hypothesis vs. Prediction: What’s the Difference?

Understanding the differences between a hypothesis and a prediction is crucial in scientific research. Often, these terms are used interchangeably, but they have distinct meanings and functions. This segment aims to clarify these differences and explain how to compose a hypothesis correctly, helping you improve the quality of your research projects.

Hypothesis: The Foundation of Your Research

A hypothesis is an educated guess about the relationship between two or more variables. It provides the basis for your research question and is a starting point for an experiment or observational study.

The critical elements for a hypothesis include:

• Specificity: A clear and concise statement that describes the relationship between variables.
• Testability: The ability to test the hypothesis through experimentation or observation.

To learn how to write a hypothesis, it’s essential to identify your research question first and then predict the relationship between the variables.

Prediction: The Expected Outcome

A prediction is a statement about a specific outcome you expect to see in your experiment or observational study. It’s derived from the hypothesis and provides a measurable way to test the relationship between variables.

Here’s an example of how to write a hypothesis and a related prediction:

• Hypothesis: Consuming a high-sugar diet leads to weight gain.
• Prediction: People who consume a high-sugar diet for six weeks will gain more weight than those who maintain a low-sugar diet during the same period.

Key Differences Between a Hypothesis and a Prediction

While a hypothesis and prediction are both essential components of scientific research, there are some key differences to keep in mind:

• A hypothesis is an educated guess that suggests a relationship between variables, while a prediction is a specific and measurable outcome based on that hypothesis.
• A hypothesis can give rise to multiple experiment or observational study predictions.

To conclude, understanding the differences between a hypothesis and a prediction, and learning how to write a hypothesis, are essential steps to form a robust foundation for your research. By creating clear, testable hypotheses along with specific, measurable predictions, you lay the groundwork for scientifically sound investigations.

Here’s a wrap-up for this guide on how to write a hypothesis. We’re confident this article was helpful for many of you. We understand that many students struggle with writing their school research . However, we hope to continue assisting you through our blog tutorial on writing different aspects of academic assignments.

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The Scientific Method

The scientific method (esgx), how science works (esgy).

Science investigation and research requires many skills and processes to come together in order to be successful and worthwhile.

• To be accepted as a science, certain methods for broadening existing knowledge, or discovering new things, are generally used.
• These methods must be repeatable and follow a logical approach.
• The methods include formulating hypotheses and carrying out investigations and experiments to test the hypothesis.
• Crucial skills are making objective observations, taking measurements, collecting information and presenting the results in the form of drawings, written explanations, tables and graphs.
• A scientist must learn to identify patterns and relationships in data.
• It is very important to then communicate these findings to the public in the form of scientific publications, at conferences, in articles or TV or radio programmes.

Watch this interesting video about "The Times and Troubles of the Scientific Method"

Video: 2CMG

The scientific method is the basic skill process in the world of science. Since the beginning of time humans have been curious as to why and how things happen in the world around us. The scientific method provides scientists with a well structured scientific platform to help find the answers to their questions. Using the scientific method there are very few things we can't investigate. Recording and writing up an investigation is an integral part of the scientific method.

What follows is a step-by-step guide to the scientific method.

1. The question (ESGZ)

Scientists are curious people, and most investigations arise from a scientist noticing something that they don't understand. Therefore the first step to any scientific investigation is:

• What is happening?
• How is it happening?
• When is it occurring?
• Why is it happening?
• Example : A farmer notices that his tomato plants that are shaded have smaller tomatoes than his plants that are in a sunny spot, which makes him wonder: 'Does the amount of sunlight a tomato plant receives affect the size of tomatoes?'

Figure 0.1: Overview of scientific method.

2. Introduction (ESG32)

Once you have a general question, background research needs to be undertaken. Your background research will ensure that you are not investigating something that has already been researched and answered. It will also tell you about interesting connections, theories, explanations and methods that people have used in the past to answer questions related to yours. Science always builds on the work of others, and it ensures that our theories are constantly improved and refined. It is important to acknowledge the work of the people upon whose work your theory relies in the form of referencing . It is also vital to communicate your findings so that future scientists can use use your work as a basis for future research.

3. Identify variables (ESG33)

Your background research will help you identify the factors that influence your question. Factors that might change during the experiment are called variables . Different types of variables are given special names. Below is a list of some important variable types:

• The dependent variable is the thing that you want to measure or investigate.
• The independent variable is a factor (or factors) that you control or change in your experiment. It will have an effect on the dependent variable.
• We call the the variables we keep constant fixed variables , or controlled variables .

Example : In this investigation, variables might include: the amount of sunshine, the types of soil in which the tomatoes are growing, the water available to each of the plants, etc. To which variable type does each factor belong?

• Dependent variable : mass of tomatoes
• Independent variable : how much light the tomato plants receive

Fixed/ Controlled variables : all tomato plants will:

• Be the same species of tomato
• Get the same fertiliser (type and amount)
• Grow in the same type of soil
• Grow in the same type of container
• Get the same amount of water
• Can you think of more?

4. Hypothesis (ESG34)

Write down a statement or prediction as to what you think will be the outcome or result of your investigation. This is your hypothesis. The hypothesis should:

• be specific
• relate directly to the question you are asking
• be expressed as a statement that includes the variables involved (the `cause' and `effect')
• be testable
• not expressed as a question but rather as a prediction
• be written in the future tense

Example : During your background research you would have learnt that tomatoes need sunshine to make food through photosynthesis. You may predict that plants that get more sun will make more food and grow bigger. In this case your hypothesis would be: I think that the more sunlight a tomato plant receives, the larger the tomatoes will grow'.

A scientific investigation does not aim to prove a particular event occurs or a particular relationship exists. Rather, an investigation shows that it cannot disprove a particular suggestion or prediction. Therefore, it is important to note that an incorrect prediction does not mean that you have failed. It means that the experiment has brought some new facts to light that you might not have thought of before. Therefore, even if your hypothesis (prediction) turns out to be wrong, DO NOT go back and change the it!

5. Aim (ESG35)

• In the aim you need to state what you going to be investigating.

Key words you can use are:

• To determine...
• To show that...
• To investigate...
• To find out...
• To observe...
• To measure...

Example : In this case, your aim would be: to investigate the effect of different amounts of sunlight on tomatoes.

In science we never `prove' a hypothesis through a single experiment because there is a chance that you made an error somewhere along the way, or there may be an alternate explanation for the results that you observe. What you can say is that your results SUPPORT the original hypothesis.

6. Apparatus (ESG36)

All the apparatus that you will need for the investigation needs to be listed.

• Sizes of beakers, test tubes and measuring cylinders
• Specialised equipment that you may need must also be included (make sure that this equipment is available for your research).
• Include all chemicals and quantities that are required for your investigation.

7. Method (ESG37)

The next step is to test your hypothesis. An experiment is a tool that you design to find out if your ideas about your question are right or wrong. You must design an experiment that accurately tests your hypothesis. The experiment is the most important part of the scientific method. We will discuss independent and dependent variables as well as controls later. These are all important concepts to know when designing an experiment. In science, another researcher may want to repeat your method, to verify your results, improve it or do a variation of your experiment. Listing the apparatus helps others to verify that you used a suitable method, and enables them to replicate the experiment.

• Write down the scientific method in bullet format for your investigation.
• The method should be written so that a complete stranger will be able to carry out the same procedure in the exact same way and get almost identical results.
• The method should be written in the past tense using the passive voice.
• the apparatus
• exact measurements or quantities of chemicals or substances
• Ensure that your method is written out in the correct sequence, with each step of the experiment numbered.
• State the criteria you will look for or measure to get results.
• Give clear instructions how the results should be recorded (table, graph etc.)
• Include safety precautions where possible.

8. Results (ESG38)

• Record your observations from doing the investigation.
• It is important that you do not write out an explanation for the results.
• Present your results in a suitable format such as tables and graphs.
• It is also important to note that not getting the result you expected is still a result. Even if there is no change at all, this is still a result that needs to be recorded.

9. Analysis of results or discussion (ESG39)

• The analysis of the results is stating in words what the results are often saying in tables/graphs.
• Discuss if there are there any relationships between your independent and dependent variables.
• It is important to look for patterns/trends in your graphs or tables and describe these clearly in words.

10. Evaluation of results (ESG3B)

• This is where you answer the question “What do the results mean?”
• Were there any unusual results? If so then these should be discussed and possible reasons for them can be given.

Discuss how you ensured the validity and reliability of the investigation.

Vailidity : Was it a fair test and did it test what it set out to test?

Reliability : If the experiment were to be repeated would the results obtained be similar?

• The best way to ensure reliability is to repeat the experiment several times and obtain an average.
• Discuss any experimental errors that may have occurred during the experiment. These can include errors in the methods and apparatus used and what make suggestions what could be done differently next time.

HOW TO WRITE A HYPOTHESIS

Writing a hypothesis

Frequently, when we hear the word ‘hypothesis’, we immediately think of an investigation in the form of a science experiment. This is not surprising, as science is the subject area where we are usually first introduced to the term.

However, the term hypothesis also applies to investigations and research in many diverse areas and branches of learning, leaving us wondering how to write a hypothesis in statistics and how to write a hypothesis in sociology alongside how to write a hypothesis in a lab report.

We can find hypotheses at work in areas as wide-ranging as history, psychology, technology, engineering, literature, design, and economics. With such a vast array of uses, hypothesis writing is an essential skill for our students to develop.

What Is a Hypothesis?

A hypothesis is a proposed or predicted answer to a question. The purpose of writing a hypothesis is to follow it up by testing that answer. This test can take the form of an investigation, experiment, or writing a research paper that will ideally prove or disprove the hypothesis’s prediction.

Despite this element of the unknown, a hypothesis is not the same thing as a guess. Though the hypothesis writer typically has some uncertainty, the creation of the hypothesis is generally based on some background knowledge and research of the topic. The writer believes in the likelihood of a specific outcome, but further investigation will be required to validate or falsify the claim made in their hypothesis.

In this regard, a hypothesis is more along the lines of an ‘educated guess’ that has been based on observation and/or background knowledge.

A hypothesis should:

• Make a prediction
• Provide reasons for that prediction
• Specifies a relationship between two or more variables
• Be testable
• Be falsifiable
• Be expressed simply and concisely
• Serves as the starting point for an investigation, an experiment, or another form of testing

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Hypothesis Examples for Students and Teachers

If students listen to classical music while studying, they will retain more information.

Mold growth is affected by the level of moisture in the air.

Students who sleep for longer at night retain more information at school.

Employees who work more than 40 hours per week show higher instances of clinical depression.

Time spent on social media is negatively correlated to the length of the average attention span.

People who spend time exercising regularly are less likely to develop a cardiovascular illness.

If people are shorter, then they are more likely to live longer.

What are Variables in a Hypothesis?

Variables are an essential aspect of any hypothesis. But what exactly do we mean by this term?

Variables are changeable factors or characteristics that may affect the outcome of an investigation. Things like age, weight, the height of participants, length of time, the difficulty of reading material, etc., could all be considered variables.

Usually, an investigation or experiment will focus on how different variables affect each other. So, it is vital to define the variables clearly if you are to measure the effect they have on each other accurately.

There are three main types of variables to consider in a hypothesis. These are:

• Independent Variables
• Dependent Variables

The Independent Variable

The independent variable is unaffected by any of the other variables in the hypothesis. We can think of the independent variable as the assumed cause .

The Dependent Variable

The dependent variable is affected by the other variables in the hypothesis. It is what is being tested or measured. We can think of the dependent variable as the assumed effect .

For example, let’s investigate the correlation between test scores across different age groups. The age groups will be the independent variable, and the test scores will be the dependent variable .

Now that we know what variables are let’s look at how they work in the various types of hypotheses.

Types of Hypotheses

There are many different types of hypotheses, and it is helpful to know the most common of these if the student selects the most suitable tool for their specific job.

The most frequently used types of hypotheses are:

The Simple Hypothesis

The complex hypothesis, the empirical hypothesis, the null hypothesis, the directional hypothesis, the non-directional hypothesis.

This straightforward hypothesis type predicts the relationship between an independent and dependent variable.

Example: Eating too much sugar causes weight gain.

This type of hypothesis is based on the relationship between multiple independent and/or dependent variables.

Example: Overeating sugar causes weight gain and poor cardiovascular health.

Also called a working hypothesis, an empirical hypothesis is tested through observation and experimentation. An empirical hypothesis is produced through investigation and trial and error. As a result, the empirical hypothesis may change its independent variables in the process.

Example: Exposure to sunlight helps lettuces grow faster.

This hypothesis states that there is no significant or meaningful relationship between specific variables.

Example: Exposure to sunlight does not affect the rate of a plant’s growth.

This type of hypothesis predicts the direction of an effect between variables, i.e., positive or negative.

Example: A high-quality education will result in a greater number of career opportunities.

Similar to the directional hypothesis, this type of hypothesis predicts the nature of the effect but not the direction that effect will go in.

Example: A high-quality education will affect the number of available career opportunities.

How to Write a Hypothesis : A STEP-BY-STEP GUIDE

• Ask a Question

The starting point for any hypothesis is asking a question. This is often called the research question . The research question is the student’s jumping-off point to developing their hypothesis. This question should be specific and answerable. The hypothesis will be the point where the research question is transformed into a declarative statement.

Ideally, the questions the students develop should be relational, i.e., they should look at how two or more variables relate to each other as described above. For example, what effect does sunlight have on the growth rate of lettuce?

• Research the Question

The research is an essential part of the process of developing a hypothesis. Students will need to examine the ideas and studies that are out there on the topic already. By examining the literature already out there on their topic, they can begin to refine their questions on the subject and begin to form predictions based on their studies.

Remember, a hypothesis can be defined as an ‘educated’ guess. This is the part of the process where the student educates themself on the subject before making their ‘guess.’

• Define Your Variables

By now, your students should be ready to form their preliminary hypotheses. To do this, they should first focus on defining their independent and dependent variables. Now may be an excellent opportunity to remind students that the independent variables are the only variables that they have complete control over, while dependent variables are what is tested or measured.

• Develop Your Preliminary Hypotheses

With variables defined, students can now work on a draft of their hypothesis. To do this, they can begin by examining their variables and the available data and then making a statement about the relationship between these variables. Students must brainstorm and reflect on what they expect to happen in their investigation before making a prediction upon which to base their hypothesis. It’s worth noting, too, that hypotheses are typically, though not exclusively, written in the present tense.

Students revisit the different types of hypotheses described earlier in this article. Students select three types of hypotheses and frame their preliminary hypotheses according to each criteria. Which works best? Which type is the least suitable for the student’s hypothesis?

• Finalize the Phrasing

By now, students will have made a decision on which type of hypothesis suits their needs best, and it will now be time to finalize the wording of their hypotheses. There are various ways that students can choose to frame their hypothesis, but below, we will examine the three most common ways.

The If/Then Phrasing

This is the most common type of hypothesis and perhaps the easiest to write for students. It follows a simple ‘ If x, then y ’ formula that makes a prediction that forms the basis of a subsequent investigation.

If I eat more calories, then I will gain weight.

Correlation Phrasing

Another way to phrase a hypothesis is to focus on the correlation between the variables. This typically takes the form of a statement that defines that relationship positively or negatively.

The more calories that are eaten beyond the daily recommended requirements, the greater the weight gain will be.

Comparison Phrasing

This form of phrasing is applicable when comparing two groups and focuses on the differences that the investigation is expected to reveal between those two groups.

Those who eat more calories will gain more weight than those who eat fewer calories.

Questions to ask during this process include:

• What tense is the hypothesis written in?
• Does the hypothesis contain both independent and dependent variables?
• Is the hypothesis framed using the if/then, correlation, or comparison framework (or other similar suitable structure)?
• Is the hypothesis worded clearly and concisely?
• Does the hypothesis make a prediction?
• Is the prediction specific?
• Is the hypothesis testable?
• Gather Data to Support/Disprove Your Hypothesis

If the purpose of a hypothesis is to provide a reason to pursue an investigation, then the student will need to gather related information together to fuel that investigation.

While, by definition, a hypothesis leans towards a specific outcome, the student shouldn’t worry if their investigations or experiments ultimately disprove their hypothesis. The hypothesis is the starting point; the destination is not preordained. This is the very essence of the scientific method. Students should trust the results of their investigation to speak for themselves. Either way, the outcome is valuable information.

TOP 10 TIPS FOR WRITING A STRONG HYPOTHESIS

• Begin by asking a clear and compelling question. Your hypothesis is a response to the inquiry you are eager to explore.
• Keep it simple and straightforward. Avoid using complex phrases or making multiple predictions in one hypothesis.
• Use the right format. A strong hypothesis is often written in the form of an “if-then” statement.
• Ensure that your hypothesis is testable. Your hypothesis should be something that can be verified through experimentation or observation.
• Stay objective. Your hypothesis should be based on facts and evidence, not personal opinions or prejudices.
• Examine different possibilities. Don’t limit yourself to just one hypothesis. Consider alternative explanations for your observations.
• Stay open to the possibility of being wrong. Your hypothesis is just a prediction, and it may not always be correct.
• Search for evidence to support your hypothesis. Investigate existing literature and gather data that supports your hypothesis.
• Make sure that your hypothesis is pertinent. Your hypothesis should be relevant to the question you are trying to investigate.
• Revise your hypothesis as necessary. If new evidence arises that contradicts your hypothesis, you may need to adjust it accordingly.

HYPOTHESIS TEACHING STRATEGIES AND ACTIVITIES

When teaching young scientists and writers, it’s essential to remember that the process of formulating a hypothesis is not always straightforward. It’s easy to make mistakes along the way, but with a bit of guidance, you can ensure your students avoid some of the most common pitfalls like these.

• Don’t let your students be too vague. Remind them that when formulating a hypothesis, it’s essential to be specific and avoid using overly general language. Make sure their hypothesis is clear and easy to understand.
• Being swayed by personal biases will impact their hypothesis negatively. It’s important to stay objective when formulating a hypothesis, so avoid letting personal biases or opinions get in the way.
• Not starting with a clear question is the number one stumbling block for students, so before forming a hypothesis, you need to reinforce the need for a clear understanding of the question they’re trying to answer. Start with a question that is specific and relevant.

Hypothesis Warmup Activity: First, organize students into small working groups of four or five. Then, set each group to collect a list of hypotheses. They can find these by searching on the Internet or finding examples in textbooks . When students have gathered together a suitable list of hypotheses, have them identify the independent and dependent variables in each case. They can underline each of these in different colors.

It may be helpful for students to examine each hypothesis to identify the ‘cause’ elements and the ‘effect’ elements. When students have finished, they can present their findings to the class.

Task 1: Set your students the task of coming up with an investigation-worthy question on a topic that interests them. This activity works particularly well for groups.

Task 2: Students search for existing information and theories on their topic on the Internet or in the library. They should take notes where necessary and begin to form an assumption or prediction based on their reading and research that they can investigate further.

Task 3: When working with a talking partner, can students identify which of their partner’s independent and dependent variables? If not, then one partner will need to revisit the definitions for the two types of variables as outlined earlier.

Task 4: Organize students into smaller groups and task them with presenting their hypotheses to each other. Students can then provide feedback before the final wording of each hypothesis is finalized.

Perhaps due to their short length, learning how to create a well-written hypothesis is not typically afforded much time in the curriculum.

However, though they are brief in length, they are complex enough to warrant focused learning and practice in class, particularly given their importance across many curriculum areas.

Learning how to write a hypothesis works well as a standalone writing skill. It can also form part of a more comprehensive academic or scientific writing study that focuses on how to write a research question, develop a theory, etc.

As with any text type, practice improves performance. By following the processes outlined above, students will be well on their way to writing their own hypotheses competently in no time.

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Formulating a Strong Hypothesis for Scientific Research

Table of Contents

A clear and focused hypothesis is one of the critical elements of a scientific research paper.

A hypothesis is a statement that outlines what the researcher intends to prove or disprove through their investigation.  Hypothesis writing in research proposal  is essential as it provides a framework for the research project and guides the entire study.

This guide will discuss the elements of a good hypothesis and the steps for writing an adequate hypothesis for your research proposal .

What Is a Research Hypothesis?

A hypothesis is an informed prediction or explanation of a phenomenon based on prior knowledge or observation. It is a tentative statement that can be tested through research and analysis. The primary purpose of a hypothesis is to guide the research process by providing a clear direction and focus for the study.

A hypothesis should be clear, concise, and specific. It should clearly state what the researcher intends to investigate and what they expect to find.

Additionally, a hypothesis should be testable and falsifiable. This means that the researcher must be able to test the hypothesis through empirical research. And that the hypothesis must be capable of being proven false if the evidence does not support it.

If the evidence does not support a hypothesis, it can still contribute to the research process. Negative results can refine and improve future research by highlighting flaws in the original hypothesis or methodology.

Some research projects may require several hypotheses that address different aspects of the research question. The number of hypotheses included in a research proposal will depend on the scope and complexity of the research project.

Focusing on one or two key hypotheses central to the research question is recommended. Including too many hypotheses can lead to a lack of focus and clarity in the research proposal.

Variables in Hypotheses

Research hypotheses propose a relationship between two or more variables.

• An independent variable: Something the researcher controls or changes.
• A dependent variable: Something the researcher observes and measures.

Example:  Regular exercise improves mental health.

The independent variable in this example is “regular exercise” – the assumed cause. The dependent variable is the degree of mental alertness – the assumed effect.

Characteristics of a Good Research Hypothesis

Hypothesis writing in research proposal requires more work than a simple guess. Your hypothesis may start with a question that might be further investigated through background research.

Ask yourself the following questions to assist you in formulating a solid study hypothesis:

• Is the wording of the hypothesis focused and clear?
• What connection exists between your hypothesis and the subject of your study?
• Is the hypothesis testable? If yes, how?
• Is there a dependent variable and an independent variable in your hypothesis?
• Does your research predict the relationship and outcome?

Hypothesis Writing in Research Proposal: 6 Steps

Writing a hypothesis for a research proposal  can be challenging, but a few key steps can help ensure it is clear, concise, and effective.

Step 1: Identify the Research Question

The first step in  writing a hypothesis in a research proposal  is to identify the research question. The research question should be clear and specific and focused on a particular area of inquiry. It should be a question that can be answered through research and analysis.

Suppose the research question is, “What is the effect of social media on mental health?”. The hypothesis might be “Increased social media use leads to higher levels of anxiety and depression.”

Step 2: Conduct Research

Your initial response to the research question should be based on the body of existing knowledge. Collect data from theories, previous studies, academic journals, experiments, and observations to make informed assumptions regarding the results of your research.

You may create a conceptual framework to ensure you’re starting with a relevant subject. This can also help you decide which variables you will research and what relationships exist between them.

Remember that you might stumble upon conflicting research as you gather background information. Don’t let this deter you or your hypothesis. Instead, frame your study and guide your hypothesis using the conflicting points.

Step 3: Formulate the Hypothesis

Once the research question has been identified, the next step is formulating the hypothesis. The hypothesis should be a statement that answers the research question and outlines what the researcher intends to prove or disprove through their investigation.

Research question : “What is the effect of social media on mental health?”

Hypothesis : “Increased social media use leads to higher levels of anxiety and depression.”

Step 4: Refine Your Hypothesis

Ensure that your hypothesis is specific and testable. Your hypothesis should have clearly defined terms and contain the following:

• Relevant variables.
• The predicted outcome of the analysis or experiment.

Step 5: Write the Hypothesis

You’ll need to write a null and alternative hypothesis if your research involves statistical hypothesis testing.

The null hypothesis proposes that there is no relationship between the variables. It is denoted by H0 and is usually a negative statement like  “Regular exercise does not improve mental health and well-being .”

The alternative hypothesis is the opposite of a null hypothesis. It proposes a relationship between the dependent and independent variables. It is written as H1 or Ha and is usually a positive statement like  “Regular exercise improves mental health and well-being.”

Step 6: Test the Hypothesis

The final step is to test your hypothesis through research and analysis. Collect data and analyze the null and alternative hypothesis to determine whether it is supported. If the evidence supports the hypothesis, it can be considered a valid explanation of the phenomenon being investigated.

Examples of Good Hypotheses

Here are a few examples of well-formulated hypotheses:

Research Question:  What is the relationship between exercise and mental health?

H1:  Regular exercise improves mental health.

H0 : Regular exercise does not improve mental health.

Research Question:  What is the effect of caffeine on cognitive performance?

H1:  Consumption of caffeine improves cognitive performance and mental alertness.

H0 : Consumption of caffeine does not improve cognitive performance and mental alertness.

Research Question:  What are the effects of sleep deprivation on cognitive function?

H1:  Sleep deprivation leads to impaired cognitive function, reduced attention span, memory, and decision-making ability.

H0 : Sleep deprivation does not affect cognitive function, attention span, memory, and decision-making ability.

A strong hypothesis is an essential element of a research proposal. A clear and concise hypothesis provides a framework for the research project and guides the entire study . 

By following the steps outlined in this article, you can craft a strong hypothesis that will guide your research. And contribute to the body of knowledge in your field.

Abir Ghenaiet

Abir is a data analyst and researcher. Among her interests are artificial intelligence, machine learning, and natural language processing. As a humanitarian and educator, she actively supports women in tech and promotes diversity.

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Over 9,500 cans of baby formula delivered to NY as part of price gouging settlement

Over 9,500 cans of baby formula were delivered to families after an investigation into illegal price gouging by Walgreens.

New York Attorney General Letitia James said in a news release Tuesday the investigation started after consumers complained about "unreasonably high prices" of formula at the pharmacy during a nationwide shortage in 2022.

In February 2022, Abbott Laboratories, a major formula manufacturer, shut down a plant that was facing contamination concerns and recalled some of its Similac products, according to James. The plant that closed, however, was responsible for producing 20% of the nation's formula products.

Coupled with supply-chain issues thanks to the ongoing COVID-19 pandemic, shelves holding baby formula in pharmacies and supermarkets were barren for months.

While the federal government enacted the Defense Production Act and passed several laws along with state governments to help provide families with the essential product, James said she issued warnings to over 30 retailers to stop overcharging for baby formula.

James said an investigation from the Office of the Attorney General found that Walgreens illegally increased the prices on at least 20 baby formula products by over 10%. Other products were raised to 20% or even 70%, according to James. As a result, Walgreens made over 2,400 sales, which amounted to over $150,000 in revenue.

“Baby formula is the main source of nutrients and absolutely essential for thousands of our most vulnerable residents, our infants. During the formula shortage, families were panicked and struggling about how to feed their babies. For Walgreens to take advantage of this crisis and jack up formula prices is not only illegal, but downright shameful,” said James. “The thousands of baby formula cans secured by my office will go directly to help New York families who need it most."

James said the settlement was resolved with the product to make it available to New York families in need, but Walgreens is still required to pay a $50,000 penalty for the findings of the investigation and will be prohibited from future price gouging.

As part of the settlement, Walgreens is required to also donate 9,564 cans of baby formula to the Met Council in Queens, and FeedMore WNY and the Buffalo Prenatal Perinatal Network in Buffalo.

James said New Yorkers who believe they are victims of price gouging should report potential concerns to the OAG  by filing a complaint online  or calling 800-771-7755.

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UK watchdog plans formal investigation into vet pricing

Large chains could be squeezing out independent practices, says Competition and Markets Authority

• Nils Pratley: competition watchdog is barking up a promising tree

The UK competition regulator is preparing to launch a formal investigation into the veterinary market after identifying “multiple concerns” in an initial review, including that pet owners may be overpaying for treatments and medicines.

The Competition and Markets Authority (CMA) said it wanted to move forward with a full review after an initial inquiry triggered an “unprecedented response” from 56,000 people, including customers and vet professionals, who raised a number of concerns about practices within the £2bn industry.

The watchdog is concerned that consumers may not be given enough information to make informed decisions over care, and that a rise in consolidation resulting in fewer independent surgeries has weakened competition.

Since 2013, about 1,500 of the 5,000 vet practices in the UK have been acquired by six of the largest corporate groups: CVS, IVC, Linnaeus, Medivet, Pets at Home and VetPartners, the CMA said.

Sarah Cardell, the CMA chief executive, said: “Our review has identified multiple concerns with the market that we think should be investigated further. These include pet owners finding it difficult to access basic information like price lists and prescription costs – and potentially overpaying for medicines.

“We are also concerned about weak competition in some areas, driven in part by sector consolidation, and the incentives for large corporate groups to act in ways which may reduce competition and choice.

“Given these strong indications of potential concern, it is time to put our work on a formal footing. We have provisionally decided to launch a market investigation because that’s the quickest route to enable us to take direct action, if needed.”

Independent vet practices accounted for 45% of UK veterinary practices in 2021, down from 89% in 2013. The number of pet owners has also increased, primarily as a result of the pandemic , with about 17m households having a furry companion in the UK.

The regulator is concerned that the larger corporations dominating the sector may be incentivised to act in a way that reduces choice for customers, leaving pet owners overpaying for medicines or prescriptions.

Owing to their scale and ability to invest in advanced equipment, larger companies may concentrate on offering more sophisticated, and therefore higher-cost, treatment that crowds out more affordable options.

Meanwhile, about 25% of pet owners were not aware they had an option to get prescriptions filled more cheaply elsewhere, meaning they were missing out on potential savings, even when accounting for prescription fees. Medicine sales make up about a quarter of the income of some vet practices, leaving little incentive to ensure clients were informed of other options.

The CMA said the situation suggested the regulatory framework may be outdated and no longer be fit for purpose.

Most of the industry’s regulations date to 1966, and primarily cover individual veterinary surgeons rather than non-vet owners, like the corporations that own the majority of sites today.

It means the Royal College of Veterinary Surgeons has limited leverage, including over transparency of pricing and and the true ownership structure of chain-owned practices.

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“The provisional view is that outcomes for consumers could be improved if regulatory requirements and/or elements of best practice could be monitored or enforced more effectively,” the CMA said.

It will run a four-week consultation, before launching a formal investigation, giving it power to potentially mandate that vets give certain information to consumers, cap prescription fees or order the sale of businesses or assets, which could include breaking up some vet chains.

The industry body, the British Veterinary Association (BVA), welcomed prospects for reform, saying that regulation was “not fit for purpose” and was failing vet teams and clients.

However, while price rises were a concern for everyone, the BVA said it was “vital to recognise there is no NHS for pets.”

“Whether they are employed by corporate or independently owned practices, vets deliver highly specialised, tailored care for the UK’s pets and the cost is a fair reflection of investment in medical equipment, supplies and medicines, and the time vet teams dedicate to the care of each patient,” the BVA president, Anna Judson, said.

“At the British Veterinary Association, we’re keen to see healthy competition and consumer choice and so we are already taking steps to support vet practices to be more transparent both in terms of costs and practice ownership. It’s important that clients have as wide a choice of vet practices as possible so they can find a service that best suits the needs of themselves and their animals,” Judson added.

“We will continue to engage constructively with the CMA by responding to this latest consultation and will continue to play a leadership role in driving positive change for vets and their clients.”

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Is Ryan Walters boosting his national profile at the expense of Oklahoma taxpayers?

Public money for Oklahoma students is paying for email pitches like this one: 

“Good morning! Ryan Walters, state superintendent of Oklahoma , banned porn/explicit reading materials and graphics from classrooms in Oklahoma, but Edmond Public Schools is appealing this action, asking the state Supreme Court to allow porn back in classrooms.”

That pitch goes on to say:

“School board members won’t even allow adults to read these books out loud, yet Edmond Public Schools are begging for kids to be reading this sexual garbage.” 

And this one, seizing on the death of an Owasso High School student : 

“An open letter called for Ryan’ (sic) immediate removal from office for, the letter claims, “fostering a culture of violence and hate against the 2SLGBTQI+ community in Oklahoma schools.

“Ryan responded to the letter saying: ‘[this is a] standard tactic of the radical left, and they will stop at nothing to destroy the country and our state.’”

“Want Ryan on to discuss?” the pitches conclude. 

Vought Strategies, a communications firm in Arlington, Virginia, sent those emails in recent weeks to national media outlets. The Oklahoma State Department of Education hired the firm to write speeches and op-eds and book Walters on at least 10 national TV and radio appearances per month. 

That has some people questioning whether Walters is boosting his national profile at the public’s expense — something Mary Vought, president and founder of the firm, made clear she is working to do.

“I will work with my network of stakeholders to obtain attendance at national events and conferences in order to increase the national exposure of the client,” she wrote in her bid for the contract, public records show. 

A former attorney general said the agency’s use of public money and resources for personal gain is concerning. 

“To have somebody whose sole purpose is not to get information to the public, but to get information about you on a public platform for your own personal gain is an illegal use of public funds and public resources, in my view,” said Drew Edmondson, a Democrat who was Oklahoma’s attorney general from 1995 to 2011. “I cannot think of a rational, legal explanation.”

State set up Vought LLC

Another question about the use of public resources was raised because secretary of state records show that David Martin, an Education Department employee, set up the LLC for Vought Strategies in Oklahoma on Nov. 9, the last day proposals were accepted. Martin is the registered agent, and he listed the Oliver Hodge Building, where the department is housed, as his address.

Martin did not respond to a voicemail left for him on Tuesday. According to his LinkedIn profile, Martin started at the department in March 2023 as assistant chief operating officer; in November, he was promoted to senior director of technology and accountability.

Dan Isett, the department’s director of communications, couldn’t explain why Martin registered the LLC.

“No, I don’t have anything for you on that,” he told KOKH.

Walters: We're doing national recruiting for teachers

Months ago, Walters,  in an interview with FOX 25 (KOKH-TV) , offered an explanation for why his department needed the public relations contract. He said his national media appearances are for teacher recruitment. 

“So you’re saying, we as Oklahoma parents and students are going to benefit from you going on more national media?” reporter Wendy Suares asked during the Nov. 13 interview.

“What I’m saying is, we are doing national recruiting efforts for teachers to come to our state,” Walters replied.  

Mary Vought, in her bid, said she has 17 years of national media experience and has pitched and orchestrated TV and radio interviews on Fox News, CNN, Bloomberg TV, Newsmax, One America News Network, Sirius XM, Salem Radio, Fox News Radio and more. 

For Vought’s work, Oklahoma agreed to pay an estimated $30,000 for the first four months, from March 6 to June 30, public records show. The contract can then be extended for up to three years. 

Vought, in a Nov. 9 letter submitted to the Office of Management and Enterprise Services, said her compensation would be $5,000 per month. She followed up on Nov. 14 with an hourly breakdown, estimating her firm will spend a total of 25 hours per week, at a rate of $200 per hour.

Vought did not respond to multiple attempts to reach her for comment. 

Vought came under fire for a similar contract with Alaska Gov. Mike Dunleavy. She was hired in 2019 for $4,000 a month in state funds to get him on Fox News and right-wing media outlets and write op-eds under his name. The contract was extended and amended multiple times through the end of Dunleavy’s second term in 2026, skirting the state’s procurement laws and playing at political favoritism,  according to Dermot Cole , a former writer for the Fairbanks Daily News-Miner, Alaska Dispatch and Alaska Dispatch News.  

The résumés of Mary Vought and Walters’ top adviser, Matt Langston, appear to overlap. Vought worked for U.S. Sen. Ron Johnson’s office from 2011-2013. Langston was Johnson’s traveling press secretary from June 2010 to Jan 2011, according to his  LinkedIn  profile.

Vought’s was the only bid received, even though the state maintains a list of a dozen Oklahoma-based public relations firms approved for state contracts.

Education Department: It's a contract extension

The contract started March 6, but emails make it clear Vought was working for Walters as early as Aug. 10. 

Isett on Tuesday told KOKH that Vought already had a contract with the department and the March 6 contract was an extension. 

“I have an email from central purchasing saying we’re in the clear to continue working with them while we were waiting for the new RFP to be approved,” Isett said. “To be clear, we wouldn’t proceed if it were extralegal.”

Isett did not provide the previous contract by publication. Oklahoma Watch’s attempt to inspect the contract in person on Wednesday was rebuffed by department staff. 

Oklahoma Watch was unable to find a record of payments to Vought in the state’s online checkbook system through Jan. 31, which is the most recent data available.

The top item on the contract shows the state and Vought agreed that, as of March 6, Vought had not yet begun the work.

“Supplier shall not commence work, commit funds, incur costs, or in any way act to obligate the state until the contract is effective,” it reads.

Vought has pitched Walters to national media outlets on topics including fentanyl and the southern border, drag queens in the classroom, People for the Ethical Treatment of Animals, teacher unions, library books, and his appointment of Chaya Raichik, the far-right social media influencer behind Libs of TikTok, to a library advisory committee.

None of the email pitches obtained by FOX 25 and Oklahoma Watch contain any reference to teacher recruitment.

The emails were sent by Caroline Woods at Vought Strategies. When reached by phone, Woods referred all questions, including Woods’ title, to Mary Vought.

Walters is in Washington, D.C., this week, making appearances on Newsmax and Fox News.

This story was produced in collaboration with FOX 25 (KOKH-TV) and Anchor/Investigative Reporter Wendy Suares.

Oklahoma Watch, at  oklahomawatch.org , is a nonprofit, nonpartisan news organization that covers public-policy issues facing the state.

Letitia James

Attorney general james delivers over 9,500 cans of baby formula to new yorkers, formula donation resolves ag james investigation into walgreens, which raised prices on baby formula during 2022 shortage, march 12, 2024.

NEW YORK – New York Attorney General Letitia James  secured 9,564 cans of baby formula, with a wholesale value of $200,000, to resolve an Office of the Attorney General (OAG) investigation into Walgreen Co. (Walgreens) which illegally raised prices of baby formula during the 2022 shortage . The OAG investigation into Walgreens price gouging of baby formula follows reports and consumer complaints about unreasonably high costs associated with this essential product. Attorney General James secured the Walgreens settlement in the form of baby formula to make it available to New Yorkers in need. In addition to the over 9,500 cans of baby formula, Walgreens will pay a $50,000 penalty to resolve the OAG investigation.

“Baby formula is the main source of nutrients and absolutely essential for thousands of our most vulnerable residents, our infants. During the formula shortage, families were panicked and struggling about how to feed their babies. For Walgreens to take advantage of this crisis and jack up formula prices is not only illegal, but downright shameful,” said Attorney General James . “The thousands of baby formula cans secured by my office will go directly to help New York families who need it most. Today's agreement should also send a clear message that my office will not tolerate any company that attempts to price gouge our state’s consumers.”

As part of the settlement with OAG, Walgreens will donate 9,564 cans of baby formula to the Met Council in Queens County, and FeedMore WNY and the Buffalo Prenatal Perinatal Network in Buffalo, Erie County. These nonprofits will distribute the formula to New Yorkers in need. Additionally, Walgreens will pay a $50,000 penalty, and will refrain from any future price gouging.

In February 2022, major baby formula manufacturer Abbott Laboratories, facing contamination concerns, shut down a plant that produced approximately 20 percent of the country’s formula and recalled several of its popular Similac formula products. The Abbott plant closure and recall contributed to a severe nationwide shortage of baby formula, which was already in short supply due to supply-chain problems caused by the COVID-19 pandemic. This acute shortage lasted through much of 2022 and led to emergency action by the federal government and state and local governments around the country. Millions of infants in the U.S. are reliant on formula as their primary or supplemental food source, and the 2022 shortage created significant hardship for families across New York State.

New York’s price gouging law prohibits vendors from unconscionably increasing prices on goods that are vital to consumers’ health, safety, or welfare during abnormal market disruptions such as the 2022 formula shortage. In May 2022, Attorney General James issued warnings to more than 30 retailers across the state to stop overcharging  for baby formula after consumers reported unreasonably high prices. An OAG investigation triggered by consumer reports found that Walgreens engaged in price gouging on at least 20 infant formula products. In the months after the Abbott recall, Walgreens raised retail prices by over 10 percent on those products. For some products, Walgreens’ price increase was over 20 percent, and in one case, it was over 70 percent. The OAG’s investigation found that Walgreens made over 2,400 individual formula sales at these inflated prices, selling over 3,400 cans or bottles of formula and bringing in over $150,000 in revenue. 

Attorney General James has been a leader in the fight to protect New York consumers and guard against price gouging. In May 2023, Attorney General James secured a $100,000 settlement with Quality King Distributors, Inc . due to unconscionable price increases for Lysol products during the early days of the COVID-19 pandemic. In March 2023, Attorney General James announced price gouging rules to protect consumers and small businesses  from corporate profiteering. The rules would strengthen enforcement of New York’s price gouging law. In April 2021, Attorney General James delivered 1.2 million eggs to food pantries  throughout the state which were secured as part of an agreement with the nation’s largest egg producers  for price gouging in the early months of the pandemic. Throughout the pandemic , during major disruptions , and ahead of declared disasters , Attorney General James has issued consumer warnings against price gouging on essential supplies.

New Yorkers should report potential concerns about price gouging to the OAG by filing a complaint online  or calling 800-771-7755.

This matter was handled by Assistant Attorney General Benjamin C. Fishman, under the supervision of Bureau Chief Jane M. Azia and Deputy Bureau Chief Laura J. Levine, all of the Consumer Frauds and Protection Bureau. Data Scientist Jasmine McAllister also assisted in this matter, under the supervision of former Directors Jonathan Werberg and Megan Thorsfeldt and Acting Director Gautam Sisodia, all of the Research and Analytics Department. The Consumer Frauds and Protection Bureau is a part of the Division for Economic Justice, which is led by Chief Deputy Attorney General Chris D’Angelo and is overseen by First Deputy Attorney General Jennifer Levy.

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Delta Air to resume flights to Israel starting June 7

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Reporting by David Shepardson; Editing by Muralikumar Anantharaman and Lincoln Feast.

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Putin on brink of six more years in power as Russians vote

Russia began three days of voting on Friday in an presidential election that is set to extend the rule of Vladimir Putin by six more years.