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How to Write a Great Hypothesis

Hypothesis Definition, 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.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "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."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. 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. At this point, researchers then 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 numerous 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 adage 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.

How to Formulate a Good Hypothesis

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.

The Importance of Operational Definitions

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.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

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 various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. 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. For example, 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.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

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 there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• 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 population sample 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."
• "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."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

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:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

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  conducting an experiment is difficult or impossible. 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 examine how the variables are related. This 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.

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.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

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."

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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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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 .

Prevent plagiarism, run a free check.

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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Research Hypothesis In Psychology: Types, & Examples

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Editor-in-Chief for Simply Psychology

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On This Page:

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.

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Developing a Hypothesis

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.3  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

• Zajonc, R. B. (1965). Social facilitation.  Science, 149 , 269–274 ↵
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Developing a Hypothesis Copyright © 2022 by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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2.4 Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.2  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be  logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be  positive.  That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

• A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
• Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
• Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
• Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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• Hiroshi Ishikawa 3  

Part of the book series: Studies in Big Data ((SBD,volume 139))

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This chapter will explain the definition and properties of a hypothesis, the related concepts, and basic methods of hypothesis generation as follows.

Describe the definition, properties, and life cycle of a hypothesis.

Describe relationships between a hypothesis and a theory, a model, and data.

Categorize and explain research questions that provide hints for hypothesis generation.

Explain how to visualize data and analysis results.

Explain the philosophy of science and scientific methods in relation to hypothesis generation in science.

Explain deduction, induction, plausible reasoning, and analogy concretely as reasoning methods useful for hypothesis generation.

Explain problem solving as hypothesis generation methods by using familiar examples.

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Ishikawa, H. (2024). Hypothesis. In: Hypothesis Generation and Interpretation. Studies in Big Data, vol 139. Springer, Cham. https://doi.org/10.1007/978-3-031-43540-9_2

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Outbreak Investigations

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Step 6: Develop Hypotheses

Step 7: evaluate hypotheses.

All Modules

As noted previously, these steps are not undertaken in a rigid serial order. In fact, the order may vary depending on the circumstances, and some steps will be undertaken simultaneously. As soon as an outbreak is suspected, one automatically considers what the cause might be and the factors that are fueling it. One of the most important steps in generating hypotheses when investigating an outbreak is to consider what is known about the biology of the disease, including it's possible modes of transmission, whether there are animal reservoirs of disease, and the length of its incubation and infectious periods. Consider this Fact Sheet for Hepatitis A:

This succinct fact sheet provides excellent clues about what to look for when investigating an outbreak of hepatitis A.

Nevertheless, once descriptive epidemiology has been conducted and information about person, place, and time is available, it is useful to reflect on the collected information in order to re-evaluate and rank hypotheses about the causes. Hypotheses are generated by consciously or subconsciously looking for differences, similarities, and correlations.

• Differences: If the frequency of disease differs in two locations or circumstances, it may be due to a factor that differs in the two circumstances.
• Similarities: If there are similarities among the cases (e.g., many reported eating at a particular restaurant), then that common factor may be the cause.
• Correlations: If the frequency of disease varies in relation to some factor, then that factor may be a cause of the disease. For example, communities with low rates of measles immunization may have high rates of measles cases.

Consider the information obtaining during hypothesis-generating interviews, and also consider the location of cases (spot map) and the time course of the epidemic in relation to the incubation period of the disease (the epidemic curve).

The next step is to evaluate the hypotheses. In some outbreaks the descriptive epidemiology rapidly points convincingly to a particular source, and further analysis is unnecessary. For example, in 1991 Massachusetts had an outbreak of vitamin D intoxication in which all of the affected cases reported drinking milk delivered to their homes by a local dairy. Inspection of the dairy revealed that excessive quantities of vitamin D were being added t the milk. However, in other situations the source is unclear, and analytic epidemiology must be utilized to more formally test the hypotheses.

There are two general study designs that can be used in analytical epidemiology: a cohort study or a case control study. Both of these evaluate specific hypotheses by comparing groups of people, but the strategies for sampling subjects for the study are very different. The following illustration summarizes the key differences between these two study designs.

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Content ©2016. All Rights Reserved. Date last modified: May 3, 2016. Wayne W. LaMorte, MD, PhD, MPH

AI-Driven Hypotheses: Real world examples exploring the potential and challenges of AI-generated hypotheses in science

Artificial intelligence (AI) is no longer confined to mere automation; it is now an active participant in the pursuit of knowledge and understanding. However, in the realm of scientific research, the integration of AI marks a significant paradigm shift, ushering in an era where machines and human actively collaborate to formulate research hypotheses and questions. While AI systems have traditionally served as powerful tools for data analysis, their evolution now allows them to go beyond analysis and generate hypotheses, prompting researchers to explore uncharted domains of research.

Let’s delve deeper in understanding this transformative capability of AI and the challenges established in research hypothesis formation, emphasizing the crucial role of human intervention throughout the AI integration process.

Table of Contents

Potential of AI-Generated Research Hypothesis: Is it enough?

The discerning ability of AI, particularly through machine learning algorithms, has demonstrated a unique capacity to identify patterns across vast datasets. This has given rise to AI systems not only proficient in analyzing existing data but also in formulating hypotheses based on patterns that may elude human observation alone. The synergy between machine-driven hypothesis generation and human expertise represents a promising frontier for scientific discovery, underscoring the importance of human oversight and interpretation.

The capability of AI to generate hypotheses raises thought-provoking questions about the nature of creativity in the research process. However, although AI can identify patterns within data, the question remains: can they exhibit true creativity in proposing hypotheses, or are they limited to recognizing patterns within existing data?

Furthermore, the intersection of AI and research transcends the generation of hypotheses to include the formulation of research questions. By actively engaging with data and recognizing gaps in knowledge, AI systems can propose insightful questions that guide researchers toward unexplored avenues. This collaborative approach between machines and researchers enhances the scope and depth of scientific inquiry, emphasizing the indispensable role of human insight in shaping the research agenda.

Challenges in AI-Driven Hypothesis Formation

Despite the immense potential, the integration of AI in hypothesis formation is not without its challenges. One significant concern is the “black box” nature of many advanced AI algorithms. As these systems become more complex, understanding the reasoning behind their generated hypotheses becomes increasingly challenging for human researchers. This lack of interpretability can hinder the acceptance of AI-driven hypotheses in the scientific community.

Moreover, biases inherent in the training data of AI models can influence the hypotheses generated. If not carefully addressed, this bias could lead to skewed perspectives and reinforce existing stereotypes. It is crucial to recognize that while AI can process vast amounts of information, it lacks the nuanced understanding and contextual awareness that human researchers bring to the table.

Real Concerns of the Scholarly Community Clouding the Integration of AI in Research Hypothesis Generation

Instance 1 In a paper published in JAMA Ophthalmology, researchers utilized GPT-4, the latest version of the language model powering ChatGPT, in conjunction with Advanced Data Analysis (ADA), a model incorporating Python for statistical analysis. The AI-generated data incorrectly suggested the superiority of one surgical procedure over another in treating keratoconus. The study aimed to demonstrate the ease with which AI could create seemingly authentic but fabricated datasets. Despite flaws detectable under scrutiny, the authors expressed concern over the potential misuse of AI in generating convincing yet false data, raising issues of research integrity. Experts emphasize the need for updated quality checks and automated tools to identify AI-generated synthetic data in scientific publishing.

Instance 2 In early October, a gathering of researchers, including a past Nobel laureate, convened in Stockholm to explore the evolving role of AI in scientific processes. Led by Hiroaki Kitano, a biologist and CEO of Sony AI, the workshop considered introducing awards for AIs and AI-human collaborations producing outstanding scientific contributions. The discussion revolved around the potential of AI in various scientific tasks, including hypothesis generation, a process traditionally requiring human creativity. While AI has long been involved in literature-based discovery and knowledge graph analysis, recent advancements, particularly in large language models, are enabling the generation of hypotheses and the exploration of unconventional ideas.

The potential for AI to make ‘alien’ hypotheses—those unlikely to be conceived by humans—has been demonstrated, raising questions about the interpretability and clarity of AI-generated hypotheses.

Ethical Considerations in AI-Driven Research Hypothesis

As AI takes on a more active role in hypothesis formation, ethical considerations become paramount. The responsible use of AI requires continuous vigilance to prevent unintended consequences. Researchers must be vigilant in identifying and mitigating biases in training data, ensuring that AI systems are not perpetuating or exacerbating existing inequalities.

Additionally, the ethical implications of AI-generated hypotheses, particularly in sensitive areas such as genetics or social sciences, demand careful scrutiny. Transparency in the decision-making process of AI algorithms is essential to build trust within the scientific community and society at large. Striking the right balance between innovation and ethical responsibility is a challenge that requires constant attention as the collaboration between humans and AI evolves.

The Human Touch: A crucial element in AI-driven research

Nuanced thinking, creativity, and contextual understanding that humans possess play a vital role in refining and validating the hypotheses generated by AI. Researchers must act as critical evaluators, questioning the assumptions made by AI algorithms and ensuring that the proposed hypotheses align with existing knowledge. Furthermore, the interpretability challenge can be addressed through interdisciplinary collaboration. Scientists working closely with experts in AI ethics, philosophy, and computer science can develop frameworks to enhance the transparency of AI-generated hypotheses. This not only fosters a better understanding of the underlying processes but also ensures that the generated hypotheses align with ethical and scientific standards.

What the Future Holds

The integration of AI in hypothesis formation is an ongoing journey with vast potential. The collaborative efforts of humans and machines hold the promise of accelerating scientific discovery, unlocking new insights, and addressing complex challenges facing humanity. However, this journey requires a balanced approach, acknowledging the strengths of AI while respecting the unique capabilities and ethical considerations that humans bring to the table.

To Conclude…

The transformative capability of AI in hypothesis formation is reshaping the landscape of scientific research. But this is not possible without the collaborative partnership between humans and machines, having the potential to drive unprecedented progress. Thus, it is imperative to navigate the challenges associated with AI integration and embrace a symbiotic relationship between human intellect and AI; with which we can unlock the full potential of this dynamic collaboration and usher in a new era of scientific exploration and understanding.

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Hypothesis Maker Online

Looking for a hypothesis maker? This online tool for students will help you formulate a beautiful hypothesis quickly, efficiently, and for free.

Are you looking for an effective hypothesis maker online? Worry no more; try our online tool for students and formulate your hypothesis within no time.

• 🔎 How to Use the Tool?
• ⚗️ What Is a Hypothesis in Science?

👍 What Does a Good Hypothesis Mean?

• 🧭 Steps to Making a Good Hypothesis

🔗 References

📄 hypothesis maker: how to use it.

Our hypothesis maker is a simple and efficient tool you can access online for free.

If you want to create a research hypothesis quickly, you should fill out the research details in the given fields on the hypothesis generator.

Below are the fields you should complete to generate your hypothesis:

• Who or what is your research based on? For instance, the subject can be research group 1.
• What does the subject (research group 1) do?
• What does the subject affect? - This shows the predicted outcome, which is the object.
• Who or what will be compared with research group 1? (research group 2).

Once you fill the in the fields, you can click the ‘Make a hypothesis’ tab and get your results.

⚗️ What Is a Hypothesis in the Scientific Method?

A hypothesis is a statement describing an expectation or prediction of your research through observation.

It is similar to academic speculation and reasoning that discloses the outcome of your scientific test . An effective hypothesis, therefore, should be crafted carefully and with precision.

A good hypothesis should have dependent and independent variables . These variables are the elements you will test in your research method – it can be a concept, an event, or an object as long as it is observable.

You can observe the dependent variables while the independent variables keep changing during the experiment.

In a nutshell, a hypothesis directs and organizes the research methods you will use, forming a large section of research paper writing.

Hypothesis vs. Theory

A hypothesis is a realistic expectation that researchers make before any investigation. It is formulated and tested to prove whether the statement is true. A theory, on the other hand, is a factual principle supported by evidence. Thus, a theory is more fact-backed compared to a hypothesis.

Another difference is that a hypothesis is presented as a single statement , while a theory can be an assortment of things . Hypotheses are based on future possibilities toward a specific projection, but the results are uncertain. Theories are verified with undisputable results because of proper substantiation.

When it comes to data, a hypothesis relies on limited information , while a theory is established on an extensive data set tested on various conditions.

You should observe the stated assumption to prove its accuracy.

Since hypotheses have observable variables, their outcome is usually based on a specific occurrence. Conversely, theories are grounded on a general principle involving multiple experiments and research tests.

This general principle can apply to many specific cases.

The primary purpose of formulating a hypothesis is to present a tentative prediction for researchers to explore further through tests and observations. Theories, in their turn, aim to explain plausible occurrences in the form of a scientific study.

It would help to rely on several criteria to establish a good hypothesis. Below are the parameters you should use to analyze the quality of your hypothesis.

🧭 6 Steps to Making a Good Hypothesis

Writing a hypothesis becomes way simpler if you follow a tried-and-tested algorithm. Let’s explore how you can formulate a good hypothesis in a few steps:

Step #1: Ask Questions

The first step in hypothesis creation is asking real questions about the surrounding reality.

Why do things happen as they do? What are the causes of some occurrences?

Your curiosity will trigger great questions that you can use to formulate a stellar hypothesis. So, ensure you pick a research topic of interest to scrutinize the world’s phenomena, processes, and events.

Step #2: Do Initial Research

Carry out preliminary research and gather essential background information about your topic of choice.

The extent of the information you collect will depend on what you want to prove.

Your initial research can be complete with a few academic books or a simple Internet search for quick answers with relevant statistics.

Still, keep in mind that in this phase, it is too early to prove or disapprove of your hypothesis.

Step #3: Identify Your Variables

Now that you have a basic understanding of the topic, choose the dependent and independent variables.

Take note that independent variables are the ones you can’t control, so understand the limitations of your test before settling on a final hypothesis.

Step #4: Formulate Your Hypothesis

You can write your hypothesis as an ‘if – then’ expression . Presenting any hypothesis in this format is reliable since it describes the cause-and-effect you want to test.

For instance: If I study every day, then I will get good grades.

Step #5: Gather Relevant Data

Once you have identified your variables and formulated the hypothesis, you can start the experiment. Remember, the conclusion you make will be a proof or rebuttal of your initial assumption.

So, gather relevant information, whether for a simple or statistical hypothesis, because you need to back your statement.

Step #6: Record Your Findings

Finally, write down your conclusions in a research paper .

Outline in detail whether the test has proved or disproved your hypothesis.

Edit and proofread your work, using a plagiarism checker to ensure the authenticity of your text.

We hope that the above tips will be useful for you. Note that if you need to conduct business analysis, you can use the free templates we’ve prepared: SWOT , PESTLE , VRIO , SOAR , and Porter’s 5 Forces .

❓ Hypothesis Formulator FAQ

Updated: Oct 25th, 2023

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Use our hypothesis maker whenever you need to formulate a hypothesis for your study. We offer a very simple tool where you just need to provide basic info about your variables, subjects, and predicted outcomes. The rest is on us. Get a perfect hypothesis in no time!

How to Generate and Validate Product Hypotheses

Every product owner knows that it takes effort to build something that'll cater to user needs. You'll have to make many tough calls if you wish to grow the company and evolve the product so it delivers more value. But how do you decide what to change in the product, your marketing strategy, or the overall direction to succeed? And how do you make a product that truly resonates with your target audience?

There are many unknowns in business, so many fundamental decisions start from a simple "what if?". But they can't be based on guesses, as you need some proof to fill in the blanks reasonably.

Because there's no universal recipe for successfully building a product, teams collect data, do research, study the dynamics, and generate hypotheses according to the given facts. They then take corresponding actions to find out whether they were right or wrong, make conclusions, and most likely restart the process again.

On this page, we thoroughly inspect product hypotheses. We'll go over what they are, how to create hypothesis statements and validate them, and what goes after this step.

What Is a Hypothesis in Product Management?

A hypothesis in product development and product management is a statement or assumption about the product, planned feature, market, or customer (e.g., their needs, behavior, or expectations) that you can put to the test, evaluate, and base your further decisions on . This may, for instance, regard the upcoming product changes as well as the impact they can result in.

A hypothesis implies that there is limited knowledge. Hence, the teams need to undergo testing activities to validate their ideas and confirm whether they are true or false.

Hypotheses guide the product development process and may point at important findings to help build a better product that'll serve user needs. In essence, teams create hypothesis statements in an attempt to improve the offering, boost engagement, increase revenue, find product-market fit quicker, or for other business-related reasons.

It's sort of like an experiment with trial and error, yet, it is data-driven and should be unbiased . This means that teams don't make assumptions out of the blue. Instead, they turn to the collected data, conducted market research , and factual information, which helps avoid completely missing the mark. The obtained results are then carefully analyzed and may influence decision-making.

Such experiments backed by data and analysis are an integral aspect of successful product development and allow startups or businesses to dodge costly startup mistakes .

‍ When do teams create hypothesis statements and validate them? To some extent, hypothesis testing is an ongoing process to work on constantly. It may occur during various product development life cycle stages, from early phases like initiation to late ones like scaling.

In any event, the key here is learning how to generate hypothesis statements and validate them effectively. We'll go over this in more detail later on.

Idea vs. Hypothesis Compared

You might be wondering whether ideas and hypotheses are the same thing. Well, there are a few distinctions.

An idea is simply a suggested proposal. Say, a teammate comes up with something you can bring to life during a brainstorming session or pitches in a suggestion like "How about we shorten the checkout process?". You can jot down such ideas and then consider working on them if they'll truly make a difference and improve the product, strategy, or result in other business benefits. Ideas may thus be used as the hypothesis foundation when you decide to prove a concept.

A hypothesis is the next step, when an idea gets wrapped with specifics to become an assumption that may be tested. As such, you can refine the idea by adding details to it. The previously mentioned idea can be worded into a product hypothesis statement like: "The cart abandonment rate is high, and many users flee at checkout. But if we shorten the checkout process by cutting down the number of steps to only two and get rid of four excessive fields, we'll simplify the user journey, boost satisfaction, and may get up to 15% more completed orders".

A hypothesis is something you can test in an attempt to reach a certain goal. Testing isn't obligatory in this scenario, of course, but the idea may be tested if you weigh the pros and cons and decide that the required effort is worth a try. We'll explain how to create hypothesis statements next.

How to Generate a Hypothesis for a Product

The last thing those developing a product want is to invest time and effort into something that won't bring any visible results, fall short of customer expectations, or won't live up to their needs. Therefore, to increase the chances of achieving a successful outcome and product-led growth , teams may need to revisit their product development approach by optimizing one of the starting points of the process: learning to make reasonable product hypotheses.

If the entire procedure is structured, this may assist you during such stages as the discovery phase and raise the odds of reaching your product goals and setting your business up for success. Yet, what's the entire process like?

• It all starts with identifying an existing problem . Is there a product area that's experiencing a downfall, a visible trend, or a market gap? Are users often complaining about something in their feedback? Or is there something you're willing to change (say, if you aim to get more profit, increase engagement, optimize a process, expand to a new market, or reach your OKRs and KPIs faster)?
• Teams then need to work on formulating a hypothesis . They put the statement into concise and short wording that describes what is expected to achieve. Importantly, it has to be relevant, actionable, backed by data, and without generalizations.
• Next, they have to test the hypothesis by running experiments to validate it (for instance, via A/B or multivariate testing, prototyping, feedback collection, or other ways).
• Then, the obtained results of the test must be analyzed . Did one element or page version outperform the other? Depending on what you're testing, you can look into various merits or product performance metrics (such as the click rate, bounce rate, or the number of sign-ups) to assess whether your prediction was correct.
• Finally, the teams can make conclusions that could lead to data-driven decisions. For example, they can make corresponding changes or roll back a step.

How Else Can You Generate Product Hypotheses?

Such processes imply sharing ideas when a problem is spotted by digging deep into facts and studying the possible risks, goals, benefits, and outcomes. You may apply various MVP tools like (FigJam, Notion, or Miro) that were designed to simplify brainstorming sessions, systemize pitched suggestions, and keep everyone organized without losing any ideas.

Besides, you can settle on one of the many frameworks that facilitate decision-making processes , ideation phases, or feature prioritization . Such frameworks are best applicable if you need to test your assumptions and structure the validation process. These are a few common ones if you're looking toward a systematic approach:

• Business Model Canvas (used to establish the foundation of the business model and helps find answers to vitals like your value proposition, finding the right customer segment, or the ways to make revenue);
• Lean Startup framework (the lean startup framework uses a diagram-like format for capturing major processes and can be handy for testing various hypotheses like how much value a product brings or assumptions on personas, the problem, growth, etc.);
• Design Thinking Process (is all about interactive learning and involves getting an in-depth understanding of the customer needs and pain points, which can be formulated into hypotheses followed by simple prototypes and tests).

Need a hand with product development?

Upsilon's team of pros is ready to share our expertise in building tech products.

How to Make a Hypothesis Statement for a Product

Once you've indicated the addressable problem or opportunity and broken down the issue in focus, you need to work on formulating the hypotheses and associated tasks. By the way, it works the same way if you want to prove that something will be false (a.k.a null hypothesis).

If you're unsure how to write a hypothesis statement, let's explore the essential steps that'll set you on the right track.

Step 1: Allocate the Variable Components

Product hypotheses are generally different for each case, so begin by pinpointing the major variables, i.e., the cause and effect . You'll need to outline what you think is supposed to happen if a change or action gets implemented.

Put simply, the "cause" is what you're planning to change, and the "effect" is what will indicate whether the change is bringing in the expected results. Falling back on the example we brought up earlier, the ineffective checkout process can be the cause, while the increased percentage of completed orders is the metric that'll show the effect.

Make sure to also note such vital points as:

• what the problem and solution are;
• what are the benefits or the expected impact/successful outcome;
• which user group is affected;
• what are the risks;
• what kind of experiments can help test the hypothesis;
• what can measure whether you were right or wrong.

Step 2: Ensure the Connection Is Specific and Logical

Mind that generic connections that lack specifics will get you nowhere. So if you're thinking about how to word a hypothesis statement, make sure that the cause and effect include clear reasons and a logical dependency .

Think about what can be the precise and link showing why A affects B. In our checkout example, it could be: fewer steps in the checkout and the removed excessive fields will speed up the process, help avoid confusion, irritate users less, and lead to more completed orders. That's much more explicit than just stating the fact that the checkout needs to be changed to get more completed orders.

Step 3: Decide on the Data You'll Collect

Certainly, multiple things can be used to measure the effect. Therefore, you need to choose the optimal metrics and validation criteria that'll best envision if you're moving in the right direction.

If you need a tip on how to create hypothesis statements that won't result in a waste of time, try to avoid vagueness and be as specific as you can when selecting what can best measure and assess the results of your hypothesis test. The criteria must be measurable and tied to the hypotheses . This can be a realistic percentage or number (say, you expect a 15% increase in completed orders or 2x fewer cart abandonment cases during the checkout phase).

Once again, if you're not realistic, then you might end up misinterpreting the results. Remember that sometimes an increase that's even as little as 2% can make a huge difference, so why make 50% the merit if it's not achievable in the first place?

Step 4: Settle on the Sequence

It's quite common that you'll end up with multiple product hypotheses. Some are more important than others, of course, and some will require more effort and input.

Therefore, just as with the features on your product development roadmap , prioritize your hypotheses according to their impact and importance. Then, group and order them, especially if the results of some hypotheses influence others on your list.

Product Hypothesis Examples

To demonstrate how to formulate your assumptions clearly, here are several more apart from the example of a hypothesis statement given above:

• Adding a wishlist feature to the cart with the possibility to send a gift hint to friends via email will increase the likelihood of making a sale and bring in additional sign-ups.
• Placing a limited-time promo code banner stripe on the home page will increase the number of sales in March.
• Moving up the call to action element on the landing page and changing the button text will increase the click-through rate twice.
• By highlighting a new way to use the product, we'll target a niche customer segment (i.e., single parents under 30) and acquire 5% more leads. 

How to Validate Hypothesis Statements: The Process Explained

There are multiple options when it comes to validating hypothesis statements. To get appropriate results, you have to come up with the right experiment that'll help you test the hypothesis. You'll need a control group or people who represent your target audience segments or groups to participate (otherwise, your results might not be accurate).

‍ What can serve as the experiment you may run? Experiments may take tons of different forms, and you'll need to choose the one that clicks best with your hypothesis goals (and your available resources, of course). The same goes for how long you'll have to carry out the test (say, a time period of two months or as little as two weeks). Here are several to get you started.

Feedback and User Testing

Talking to users, potential customers, or members of your own online startup community can be another way to test your hypotheses. You may use surveys, questionnaires, or opt for more extensive interviews to validate hypothesis statements and find out what people think. This assumption validation approach involves your existing or potential users and might require some additional time, but can bring you many insights.

Conduct A/B or Multivariate Tests

One of the experiments you may develop involves making more than one version of an element or page to see which option resonates with the users more. As such, you can have a call to action block with different wording or play around with the colors, imagery, visuals, and other things.

To run such split experiments, you can apply tools like VWO that allows to easily construct alternative designs and split what your users see (e.g., one half of the users will see version one, while the other half will see version two). You can track various metrics and apply heatmaps, click maps, and screen recordings to learn more about user response and behavior. Mind, though, that the key to such tests is to get as many users as you can give the tests time. Don't jump to conclusions too soon or if very few people participated in your experiment.

Build Prototypes and Fake Doors

Demos and clickable prototypes can be a great way to save time and money on costly feature or product development. A prototype also allows you to refine the design. However, they can also serve as experiments for validating hypotheses, collecting data, and getting feedback.

For instance, if you have a new feature in mind and want to ensure there is interest, you can utilize such MVP types as fake doors . Make a short demo recording of the feature and place it on your landing page to track interest or test how many people sign up.

Usability Testing

Similarly, you can run experiments to observe how users interact with the feature, page, product, etc. Usually, such experiments are held on prototype testing platforms with a focus group representing your target visitors. By showing a prototype or early version of the design to users, you can view how people use the solution, where they face problems, or what they don't understand. This may be very helpful if you have hypotheses regarding redesigns and user experience improvements before you move on from prototype to MVP development.

You can even take it a few steps further and build a barebone feature version that people can really interact with, yet you'll be the one behind the curtain to make it happen. There were many MVP examples when companies applied Wizard of Oz or concierge MVPs to validate their hypotheses.

Or you can actually develop some functionality but release it for only a limited number of people to see. This is referred to as a feature flag , which can show really specific results but is effort-intensive. 

What Comes After Hypothesis Validation?

Analysis is what you move on to once you've run the experiment. This is the time to review the collected data, metrics, and feedback to validate (or invalidate) the hypothesis.

You have to evaluate the experiment's results to determine whether your product hypotheses were valid or not. For example, if you were testing two versions of an element design, color scheme, or copy, look into which one performed best.

It is crucial to be certain that you have enough data to draw conclusions, though, and that it's accurate and unbiased . Because if you don't, this may be a sign that your experiment needs to be run for some additional time, be altered, or held once again. You won't want to make a solid decision based on uncertain or misleading results, right?

• If the hypothesis was supported , proceed to making corresponding changes (such as implementing a new feature, changing the design, rephrasing your copy, etc.). Remember that your aim was to learn and iterate to improve.
• If your hypothesis was proven false , think of it as a valuable learning experience. The main goal is to learn from the results and be able to adjust your processes accordingly. Dig deep to find out what went wrong, look for patterns and things that may have skewed the results. But if all signs show that you were wrong with your hypothesis, accept this outcome as a fact, and move on. This can help you make conclusions on how to better formulate your product hypotheses next time. Don't be too judgemental, though, as a failed experiment might only mean that you need to improve the current hypothesis, revise it, or create a new one based on the results of this experiment, and run the process once more.

On another note, make sure to record your hypotheses and experiment results . Some companies use CRMs to jot down the key findings, while others use something as simple as Google Docs. Either way, this can be your single source of truth that can help you avoid running the same experiments or allow you to compare results over time.

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Final Thoughts on Product Hypotheses

The hypothesis-driven approach in product development is a great way to avoid uncalled-for risks and pricey mistakes. You can back up your assumptions with facts, observe your target audience's reactions, and be more certain that this move will deliver value.

However, this only makes sense if the validation of hypothesis statements is backed by relevant data that'll allow you to determine whether the hypothesis is valid or not. By doing so, you can be certain that you're developing and testing hypotheses to accelerate your product management and avoiding decisions based on guesswork.

Certainly, a failed experiment may bring you just as much knowledge and findings as one that succeeds. Teams have to learn from their mistakes, boost their hypothesis generation and testing knowledge, and make improvements according to the results of their experiments. This is an ongoing process, of course, as no product can grow if it isn't iterated and improved.

If you're only planning to or are currently building a product, Upsilon can lend you a helping hand. Our team has years of experience providing product development services for growth-stage startups and building MVPs for early-stage businesses , so you can use our expertise and knowledge to dodge many mistakes. Don't be shy to contact us to discuss your needs! 

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Hypothesis-generating and confirmatory studies, Bonferroni correction, and pre-specification of trial endpoints

A p-value presents the outcome of a statistically tested null hypothesis. It indicates how incompatible observed data are with a statistical model defined by a null hypothesis. This hypothesis can, for example, be that 2 parameters have identical values, or that they differ by a specified amount. A low p-value shows that it is unlikely (a high p-value that it is not unlikely) that the observed data are consistent with the null hypothesis. Many null hypotheses are tested in order to generate study hypotheses for further research, others to confirm an already established study hypothesis. The difference between generating and confirming a hypothesis is crucial for the interpretation of the results. Presenting an outcome from a hypothesis-generating study as if it had been produced in a confirmatory study is misleading and represents methodological ignorance or scientific misconduct.

Hypothesis-generating studies differ methodologically from confirmatory studies. A generated hypothesis must be confirmed in a new study. An experiment is usually required for confirmation as an observational study cannot provide unequivocal results. For example, selection and confounding bias can be prevented by randomization and blinding in a clinical trial, but not in an observational study. Confirmatory studies, but not hypothesis-generating studies, also require control of the inflation in the false-positive error risk that is caused by testing multiple null hypotheses. The phenomenon is known as a multiplicity or mass-significance effect. A method for correcting the significance level for the multiplicity effect has been devised by the Italian mathematician Carlo Emilio Bonferroni. The correction (Bender and Lange 2001 ) is often misused in hypothesis-generating studies, often ignored when designing confirmatory studies (which results in underpowered studies), and often inadequately used in laboratory studies, for example when an investigator corrects the significance level for comparing 3 experimental groups by lowering it to 0.05/3 = 0. 017 and believes that this solves the problem of testing 50 null hypotheses, which would have required a corrected significance level of 0.05/50 = 0.001.

In a confirmatory study, it is mandatory to show that the tested hypothesis has been pre-specified. A study protocol or statistical analysis plan should therefore be enclosed with the study report when submitted to a scientific journal for publication. Since 2005 the ICMJE (International Committee of Medical Journal Editors) and the WHO also require registration of clinical trials and their endpoints in a publicly accessible register before enrollment of the first participant. Changing endpoints in a randomized trial after its initiation can in some cases be acceptable, but this is never a trivial problem (Evans 2007 ) and must always be described to the reader. Many authors do not understand the importance of pre-specification and desist from registering their trial, use vague or ambiguous endpoint definitions, redefine the primary endpoint during the analysis, switch primary and secondary outcomes, or present completely new endpoints without mentioning this to the reader. Such publications are simply not credible, but are nevertheless surprisingly common (Ramagopalan et al. 2014 ) even in high impact factor journals (Goldacre et al. 2019 ). A serious editorial evaluation of manuscripts presenting confirmatory results should always include a verification of the endpoint’s pre-specification.

Hypothesis-generating studies are much more common than confirmatory, because the latter are logistically more complex, more laborious, more time-consuming, more expensive, and require more methodological expertise. However, the result of a hypothesis-generating study is just a hypothesis. A hypothesis cannot be generated and confirmed in the same study, and it cannot be confirmed with a new hypothesis-generating study. Confirmatory studies are essential for scientific progress.

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