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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

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 .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, 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 types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

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

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Step 1. Ask a question

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

Step 2. Do some preliminary research

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

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize 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.

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

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.

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 .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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

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

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.

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McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved April 5, 2024, from https://www.scribbr.com/methodology/hypothesis/

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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

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

• Collecting Data

Frequently Asked Questions

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

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

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

To form a hypothesis, you should take these steps:

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

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

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

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

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

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

A Word From Verywell

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

Some examples of how to write a hypothesis include:

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

The four parts of a hypothesis are:

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

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

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

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

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• Knowledge Base
• Methodology
• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

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

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

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Step 1: ask a question.

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

Step 2: Do some preliminary research

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

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

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

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

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

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McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 2 April 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

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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 Develop a Good Research Hypothesis

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

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

Table of Contents

What is Hypothesis?

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

What is a Research Hypothesis?

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

Characteristics of a Good Research Hypothesis

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

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

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

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

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

Source: Educational Hub

How to formulate a research hypothesis.

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

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

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

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

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

3. Define the variables

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

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

4. Scrutinize the hypothesis

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

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

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

2. Complex Hypothesis

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

3. Directional Hypothesis

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

4. Non-directional Hypothesis

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

5. Associative and Causal Hypothesis

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

6. Null Hypothesis

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

7. Alternative Hypothesis

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

Research Hypothesis Examples of Independent and Dependent Variables

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

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

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

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

Importance of a Testable Hypothesis

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

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

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

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

Frequently Asked Questions

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

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

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

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

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

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

Thanks a lot for your valuable guidance.

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

Useful piece!

This is awesome.Wow.

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

Nicely explained

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

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

clear and concise. thanks.

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Good to learn

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

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How to Write a Research Hypothesis

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Since grade school, we've all been familiar with hypotheses. The hypothesis is an essential step of the scientific method. But what makes an effective research hypothesis, how do you create one, and what types of hypotheses are there? We answer these questions and more.

Updated on April 27, 2022

What is a research hypothesis?

General hypothesis.

Since grade school, we've all been familiar with the term “hypothesis.” A hypothesis is a fact-based guess or prediction that has not been proven. It is an essential step of the scientific method. The hypothesis of a study is a drive for experimentation to either prove the hypothesis or dispute it.

Research Hypothesis

A research hypothesis is more specific than a general hypothesis. It is an educated, expected prediction of the outcome of a study that is testable.

What makes an effective research hypothesis?

A good research hypothesis is a clear statement of the relationship between a dependent variable(s) and independent variable(s) relevant to the study that can be disproven.

Research hypothesis checklist

Once you've written a possible hypothesis, make sure it checks the following boxes:

• It must be testable: You need a means to prove your hypothesis. If you can't test it, it's not a hypothesis.
• It must include a dependent and independent variable: At least one independent variable ( cause ) and one dependent variable ( effect ) must be included.
• The language must be easy to understand: Be as clear and concise as possible. Nothing should be left to interpretation.
• It must be relevant to your research topic: You probably shouldn't be talking about cats and dogs if your research topic is outer space. Stay relevant to your topic.

How to create an effective research hypothesis

Pose it as a question first.

Start your research hypothesis from a journalistic approach. Ask one of the five W's: Who, what, when, where, or why.

A possible initial question could be: Why is the sky blue?

Do the preliminary research

Once you have a question in mind, read research around your topic. Collect research from academic journals.

If you're looking for information about the sky and why it is blue, research information about the atmosphere, weather, space, the sun, etc.

Write a draft hypothesis

Once you're comfortable with your subject and have preliminary knowledge, create a working hypothesis. Don't stress much over this. Your first hypothesis is not permanent. Look at it as a draft.

Your first draft of a hypothesis could be: Certain molecules in the Earth's atmosphere are responsive to the sky being the color blue.

Make your working draft perfect

Take your working hypothesis and make it perfect. Narrow it down to include only the information listed in the “Research hypothesis checklist” above.

Now that you've written your working hypothesis, narrow it down. Your new hypothesis could be: Light from the sun hitting oxygen molecules in the sky makes the color of the sky appear blue.

Write a null hypothesis

Your null hypothesis should be the opposite of your research hypothesis. It should be able to be disproven by your research.

In this example, your null hypothesis would be: Light from the sun hitting oxygen molecules in the sky does not make the color of the sky appear blue.

Why is it important to have a clear, testable hypothesis?

One of the main reasons a manuscript can be rejected from a journal is because of a weak hypothesis. “Poor hypothesis, study design, methodology, and improper use of statistics are other reasons for rejection of a manuscript,” says Dr. Ish Kumar Dhammi and Dr. Rehan-Ul-Haq in Indian Journal of Orthopaedics.

According to Dr. James M. Provenzale in American Journal of Roentgenology , “The clear declaration of a research question (or hypothesis) in the Introduction is critical for reviewers to understand the intent of the research study. It is best to clearly state the study goal in plain language (for example, “We set out to determine whether condition x produces condition y.”) An insufficient problem statement is one of the more common reasons for manuscript rejection.”

Characteristics that make a hypothesis weak include:

• Unclear variables
• Unoriginality
• Too general
• Too specific

A weak hypothesis leads to weak research and methods . The goal of a paper is to prove or disprove a hypothesis - or to prove or disprove a null hypothesis. If the hypothesis is not a dependent variable of what is being studied, the paper's methods should come into question.

A strong hypothesis is essential to the scientific method. A hypothesis states an assumed relationship between at least two variables and the experiment then proves or disproves that relationship with statistical significance. Without a proven and reproducible relationship, the paper feeds into the reproducibility crisis. Learn more about writing for reproducibility .

In a study published in The Journal of Obstetrics and Gynecology of India by Dr. Suvarna Satish Khadilkar, she reviewed 400 rejected manuscripts to see why they were rejected. Her studies revealed that poor methodology was a top reason for the submission having a final disposition of rejection.

Aside from publication chances, Dr. Gareth Dyke believes a clear hypothesis helps efficiency.

“Developing a clear and testable hypothesis for your research project means that you will not waste time, energy, and money with your work,” said Dyke. “Refining a hypothesis that is both meaningful, interesting, attainable, and testable is the goal of all effective research.”

Types of research hypotheses

There can be overlap in these types of hypotheses.

Simple hypothesis

A simple hypothesis is a hypothesis at its most basic form. It shows the relationship of one independent and one independent variable.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable).

Complex hypothesis

A complex hypothesis shows the relationship of two or more independent and dependent variables.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable) and heart disease (dependent variable).

Directional hypothesis

A directional hypothesis guesses which way the results of an experiment will go. It uses words like increase, decrease, higher, lower, positive, negative, more, or less. It is also frequently used in statistics.

Example: Humans exposed to radiation have a higher risk of cancer than humans not exposed to radiation.

Non-directional hypothesis

A non-directional hypothesis says there will be an effect on the dependent variable, but it does not say which direction.

Associative hypothesis

An associative hypothesis says that when one variable changes, so does the other variable.

Alternative hypothesis

An alternative hypothesis states that the variables have a relationship.

• The opposite of a null hypothesis

Example: An apple a day keeps the doctor away.

Null hypothesis

A null hypothesis states that there is no relationship between the two variables. It is posed as the opposite of what the alternative hypothesis states.

Researchers use a null hypothesis to work to be able to reject it. A null hypothesis:

• Can never be proven
• Can only be rejected
• Is the opposite of an alternative hypothesis

Example: An apple a day does not keep the doctor away.

Logical hypothesis

A logical hypothesis is a suggested explanation while using limited evidence.

Example: Bats can navigate in the dark better than tigers.

In this hypothesis, the researcher knows that tigers cannot see in the dark, and bats mostly live in darkness.

Empirical hypothesis

An empirical hypothesis is also called a “working hypothesis.” It uses the trial and error method and changes around the independent variables.

• An apple a day keeps the doctor away.
• Two apples a day keep the doctor away.
• Three apples a day keep the doctor away.

In this case, the research changes the hypothesis as the researcher learns more about his/her research.

Statistical hypothesis

A statistical hypothesis is a look of a part of a population or statistical model. This type of hypothesis is especially useful if you are making a statement about a large population. Instead of having to test the entire population of Illinois, you could just use a smaller sample of people who live there.

Example: 70% of people who live in Illinois are iron deficient.

Causal hypothesis

A causal hypothesis states that the independent variable will have an effect on the dependent variable.

Example: Using tobacco products causes cancer.

Final thoughts

Make sure your research is error-free before you send it to your preferred journal . Check our our English Editing services to avoid your chances of desk rejection.

Jonny Rhein, BA

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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What is and How to Write a Good Hypothesis in Research?

• 4 minute read
• 289.1K views

Table of Contents

One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

Language Editing Plus

Elsevier’s Language Editing Plus service can help ensure that your research hypothesis is well-designed, and articulates your research and conclusions. Our most comprehensive editing package, you can count on a thorough language review by native-English speakers who are PhDs or PhD candidates. We’ll check for effective logic and flow of your manuscript, as well as document formatting for your chosen journal, reference checks, and much more.

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The Research Hypothesis: Role and Construction

• Phyllis G. Supino EdD 3  
• First Online: 01 January 2012

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A hypothesis is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator’s thinking about the problem and, therefore, facilitates a solution. There are three primary modes of inference by which hypotheses are developed: deduction (reasoning from a general propositions to specific instances), induction (reasoning from specific instances to a general proposition), and abduction (formulation/acceptance on probation of a hypothesis to explain a surprising observation).

A research hypothesis should reflect an inference about variables; be stated as a grammatically complete, declarative sentence; be expressed simply and unambiguously; provide an adequate answer to the research problem; and be testable. Hypotheses can be classified as conceptual versus operational, single versus bi- or multivariable, causal or not causal, mechanistic versus nonmechanistic, and null or alternative. Hypotheses most commonly entail statements about “variables” which, in turn, can be classified according to their level of measurement (scaling characteristics) or according to their role in the hypothesis (independent, dependent, moderator, control, or intervening).

A hypothesis is rendered operational when its broadly (conceptually) stated variables are replaced by operational definitions of those variables. Hypotheses stated in this manner are called operational hypotheses, specific hypotheses, or predictions and facilitate testing.

• Attention Deficit Hyperactivity Disorder
• Operational Definition
• Moderator Variable
• Ventricular Performance
• Attention Deficit Hyperactivity Disorder Group

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Wrong hypotheses, rightly worked from, have produced more results than unguided observation

—Augustus De Morgan, 1872[ 1 ]—

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Supino, P.G. (2012). The Research Hypothesis: Role and Construction. In: Supino, P., Borer, J. (eds) Principles of Research Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3360-6_3

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

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

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Research Hypothesis: What It Is, Types + How to Develop?

A research study starts with a question. Researchers worldwide ask questions and create research hypotheses. The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones.

In this blog, we’ll learn what a research hypothesis is, why it’s important in research, and the different types used in science. We’ll also guide you through creating your research hypothesis and discussing ways to test and evaluate it.

What is a Research Hypothesis?

A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.

It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.

A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.

Importance of Hypothesis in Research

Hypotheses are really important in research. They help design studies, allow for practical testing, and add to our scientific knowledge. Their main role is to organize research projects, making them purposeful, focused, and valuable to the scientific community. Let’s look at some key reasons why they matter:

• A research hypothesis helps test theories.

A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior.

• It serves as a great platform for investigation activities.

It serves as a launching pad for investigation activities, which offers researchers a clear starting point. A research hypothesis can explore the relationship between exercise and stress reduction.

• Hypothesis guides the research work or study.

A well-formulated hypothesis guides the entire research process. It ensures that the study remains focused and purposeful. For instance, a hypothesis about the impact of social media on interpersonal relationships provides clear guidance for a study.

• Hypothesis sometimes suggests theories.

In some cases, a hypothesis can suggest new theories or modifications to existing ones. For example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration of current medical theories.

• It helps in knowing the data needs.

A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the necessary information—a hypothesis guiding the collection of demographic data to analyze the influence of age on a particular phenomenon.

• The hypothesis explains social phenomena.

Hypotheses are instrumental in explaining complex social phenomena. For instance, a hypothesis might explore the relationship between economic factors and crime rates in a given community.

• Hypothesis provides a relationship between phenomena for empirical Testing.

Hypotheses establish clear relationships between phenomena, paving the way for empirical testing. An example could be a hypothesis exploring the correlation between sleep patterns and academic performance.

• It helps in knowing the most suitable analysis technique.

A hypothesis guides researchers in selecting the most appropriate analysis techniques for their data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead to the choice of statistical analyses best suited for educational research.

Characteristics of a Good Research Hypothesis

A hypothesis is a specific idea that you can test in a study. It often comes from looking at past research and theories. A good hypothesis usually starts with a research question that you can explore through background research. For it to be effective, consider these key characteristics:

• Clear and Focused Language: A good hypothesis uses clear and focused language to avoid confusion and ensure everyone understands it.
• Related to the Research Topic: The hypothesis should directly relate to the research topic, acting as a bridge between the specific question and the broader study.
• Testable: An effective hypothesis can be tested, meaning its prediction can be checked with real data to support or challenge the proposed relationship.
• Potential for Exploration: A good hypothesis often comes from a research question that invites further exploration. Doing background research helps find gaps and potential areas to investigate.
• Includes Variables: The hypothesis should clearly state both the independent and dependent variables, specifying the factors being studied and the expected outcomes.
• Ethical Considerations: Check if variables can be manipulated without breaking ethical standards. It’s crucial to maintain ethical research practices.
• Predicts Outcomes: The hypothesis should predict the expected relationship and outcome, acting as a roadmap for the study and guiding data collection and analysis.
• Simple and Concise: A good hypothesis avoids unnecessary complexity and is simple and concise, expressing the essence of the proposed relationship clearly.
• Clear and Assumption-Free: The hypothesis should be clear and free from assumptions about the reader’s prior knowledge, ensuring universal understanding.
• Observable and Testable Results: A strong hypothesis implies research that produces observable and testable results, making sure the study’s outcomes can be effectively measured and analyzed.

When you use these characteristics as a checklist, it can help you create a good research hypothesis. It’ll guide improving and strengthening the hypothesis, identifying any weaknesses, and making necessary changes. Crafting a hypothesis with these features helps you conduct a thorough and insightful research study.

Types of Research Hypotheses

The research hypothesis comes in various types, each serving a specific purpose in guiding the scientific investigation. Knowing the differences will make it easier for you to create your own hypothesis. Here’s an overview of the common types:

01. Null Hypothesis

The null hypothesis states that there is no connection between two considered variables or that two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable, verifiable, and can be rejected.

For example, if you’re studying the relationship between Project A and Project B, assuming both projects are of equal standard is your null hypothesis. It needs to be specific for your study.

02. Alternative Hypothesis

The alternative hypothesis is basically another option to the null hypothesis. It involves looking for a significant change or alternative that could lead you to reject the null hypothesis. It’s a different idea compared to the null hypothesis.

When you create a null hypothesis, you’re making an educated guess about whether something is true or if there’s a connection between that thing and another variable. If the null view suggests something is correct, the alternative hypothesis says it’s incorrect. 

For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative hypothesis would be “I’m not going to get $1000 or be richer.”

03. Directional Hypothesis

The directional hypothesis predicts the direction of the relationship between independent and dependent variables. They specify whether the effect will be positive or negative.

If you increase your study hours, you will experience a positive association with your exam scores. This hypothesis suggests that as you increase the independent variable (study hours), there will also be an increase in the dependent variable (exam scores).

04. Non-directional Hypothesis

The non-directional hypothesis predicts the existence of a relationship between variables but does not specify the direction of the effect. It suggests that there will be a significant difference or relationship, but it does not predict the nature of that difference.

For example, you will find no notable difference in test scores between students who receive the educational intervention and those who do not. However, once you compare the test scores of the two groups, you will notice an important difference.

05. Simple Hypothesis

A simple hypothesis predicts a relationship between one dependent variable and one independent variable without specifying the nature of that relationship. It’s simple and usually used when we don’t know much about how the two things are connected.

For example, if you adopt effective study habits, you will achieve higher exam scores than those with poor study habits.

06. Complex Hypothesis

A complex hypothesis is an idea that specifies a relationship between multiple independent and dependent variables. It is a more detailed idea than a simple hypothesis.

While a simple view suggests a straightforward cause-and-effect relationship between two things, a complex hypothesis involves many factors and how they’re connected to each other.

For example, when you increase your study time, you tend to achieve higher exam scores. The connection between your study time and exam performance is affected by various factors, including the quality of your sleep, your motivation levels, and the effectiveness of your study techniques.

If you sleep well, stay highly motivated, and use effective study strategies, you may observe a more robust positive correlation between the time you spend studying and your exam scores, unlike those who may lack these factors.

07. Associative Hypothesis

An associative hypothesis proposes a connection between two things without saying that one causes the other. Basically, it suggests that when one thing changes, the other changes too, but it doesn’t claim that one thing is causing the change in the other.

For example, you will likely notice higher exam scores when you increase your study time. You can recognize an association between your study time and exam scores in this scenario.

Your hypothesis acknowledges a relationship between the two variables—your study time and exam scores—without asserting that increased study time directly causes higher exam scores. You need to consider that other factors, like motivation or learning style, could affect the observed association.

08. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests that changes in one variable directly cause changes in another variable.

For example, when you increase your study time, you experience higher exam scores. This hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you spend studying, the higher your exam scores. It assumes that changes in your study time directly influence changes in your exam performance.

09. Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence. If an experiment proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement more reliable than a wild guess.

For example, if you increase the dosage of a certain medication, you might observe a quicker recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are given varying dosages of the medication, and you measure and compare their recovery times. This allows you to directly see the effects of different dosages on how fast patients recover.

This way, you can create a research hypothesis: “Increasing the dosage of a certain medication will lead to a faster recovery time for patients.”

10. Statistical Hypothesis

A statistical hypothesis is a statement or assumption about a population parameter that is the subject of an investigation. It serves as the basis for statistical analysis and testing. It is often tested using statistical methods to draw inferences about the larger population.

In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.

For example, let’s say you’re testing a new medicine. Your hypothesis could be that the medicine doesn’t really help patients get better. So, you collect data and use statistics to see if your guess is right or if the medicine actually makes a difference.

If the data strongly shows that the medicine does help, you say your guess was wrong, and the medicine does make a difference. But if the proof isn’t strong enough, you can stick with your original guess because you didn’t get enough evidence to change your mind.

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

Define the area of interest or the problem you want to investigate. Make sure it’s clear and well-defined.

Start by asking a question about your chosen topic. Consider the limitations of your research and create a straightforward problem related to your topic. Once you’ve done that, you can develop and test a hypothesis with evidence.

Step 2: Conduct a literature review

Review existing literature related to your research problem. This will help you understand the current state of knowledge in the field, identify gaps, and build a foundation for your hypothesis. Consider the following questions:

• What existing research has been conducted on your chosen topic?
• Are there any gaps or unanswered questions in the current literature?
• How will the existing literature contribute to the foundation of your research?

Step 3: Formulate your research question

Based on your literature review, create a specific and concise research question that addresses your identified problem. Your research question should be clear, focused, and relevant to your field of study.

Step 4: Identify variables

Determine the key variables involved in your research question. Variables are the factors or phenomena that you will study and manipulate to test your hypothesis.

• Independent Variable: The variable you manipulate or control.
• Dependent Variable: The variable you measure to observe the effect of the independent variable.

Step 5: State the Null hypothesis

The null hypothesis is a statement that there is no significant difference or effect. It serves as a baseline for comparison with the alternative hypothesis.

Step 6: Select appropriate methods for testing the hypothesis

Choose research methods that align with your study objectives, such as experiments, surveys, or observational studies. The selected methods enable you to test your research hypothesis effectively.

Creating a research hypothesis usually takes more than one try. Expect to make changes as you collect data. It’s normal to test and say no to a few hypotheses before you find the right answer to your research question.

Testing and Evaluating Hypotheses

Testing hypotheses is a really important part of research. It’s like the practical side of things. Here, real-world evidence will help you determine how different things are connected. Let’s explore the main steps in hypothesis testing:

• State your research hypothesis.

Before testing, clearly articulate your research hypothesis. This involves framing both a null hypothesis, suggesting no significant effect or relationship, and an alternative hypothesis, proposing the expected outcome.

• Collect data strategically.

Plan how you will gather information in a way that fits your study. Make sure your data collection method matches the things you’re studying.

Whether through surveys, observations, or experiments, this step demands precision and adherence to the established methodology. The quality of data collected directly influences the credibility of study outcomes.

• Perform an appropriate statistical test.

Choose a statistical test that aligns with the nature of your data and the hypotheses being tested. Whether it’s a t-test, chi-square test, ANOVA, or regression analysis, selecting the right statistical tool is paramount for accurate and reliable results.

• Decide if your idea was right or wrong.

Following the statistical analysis, evaluate the results in the context of your null hypothesis. You need to decide if you should reject your null hypothesis or not.

• Share what you found.

When discussing what you found in your research, be clear and organized. Say whether your idea was supported or not, and talk about what your results mean. Also, mention any limits to your study and suggest ideas for future research.

The Role of QuestionPro to Develop a Good Research Hypothesis

QuestionPro is a survey and research platform that provides tools for creating, distributing, and analyzing surveys. It plays a crucial role in the research process, especially when you’re in the initial stages of hypothesis development. Here’s how QuestionPro can help you to develop a good research hypothesis:

• Survey design and data collection: You can use the platform to create targeted questions that help you gather relevant data.
• Exploratory research: Through surveys and feedback mechanisms on QuestionPro, you can conduct exploratory research to understand the landscape of a particular subject.
• Literature review and background research: QuestionPro surveys can collect sample population opinions, experiences, and preferences. This data and a thorough literature evaluation can help you generate a well-grounded hypothesis by improving your research knowledge.
• Identifying variables: Using targeted survey questions, you can identify relevant variables related to their research topic.
• Testing assumptions: You can use surveys to informally test certain assumptions or hypotheses before formalizing a research hypothesis.
• Data analysis tools: QuestionPro provides tools for analyzing survey data. You can use these tools to identify the collected data’s patterns, correlations, or trends.
• Refining your hypotheses: As you collect data through QuestionPro, you can adjust your hypotheses based on the real-world responses you receive.

A research hypothesis is like a guide for researchers in science. It’s a well-thought-out idea that has been thoroughly tested. This idea is crucial as researchers can explore different fields, such as medicine, social sciences, and natural sciences. The research hypothesis links theories to real-world evidence and gives researchers a clear path to explore and make discoveries.

QuestionPro Research Suite is a helpful tool for researchers. It makes creating surveys, collecting data, and analyzing information easily. It supports all kinds of research, from exploring new ideas to forming hypotheses. With a focus on using data, it helps researchers do their best work.

Are you interested in learning more about QuestionPro Research Suite? Take advantage of QuestionPro’s free trial to get an initial look at its capabilities and realize the full potential of your research efforts.

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Original Research: Creating a Hypothesis

• Initial Steps

Creating a Hypothesis

• Research Designs and Methods
• Submitting a Research Plan for Review
• Performing the Research
• Analyzing the Data
• Writing the Research Paper

After following the initial steps, the researcher should be able to create a hypothesis that can be tested. A hypothesis is a proposed statement that is intended to explain a theory for why something happens. To create a solid hypothesis, make sure it is not listed as a question, but as a prediction statement. To create a research hypothesis there has to be both a dependent and independent variable, and an expected outcome. Independent variables are what may be changed in the experiment to create an outcome. The dependent variable is what the experiment is intended to measure based on changes made to the independent variable. Defining the expected outcome creates the predictive component of the hypothesis that can be tested. Incorporating these elements into a simple predictive statement ensures that you can determine an outcome from the experiment. Ensure that any variables are taken into consideration, and that the results from the hypothesis are measurable.

Types of Hypotheses

There are many types of hypotheses, but the seven most common are the following:

• Simple Hypothesis - Questions the relationship between the dependent and independent variables.
• Complex Hypothesis - Questions the effect of multiple dependent and independent variables.
• Empirical Hypothesis - Often called a working hypothesis, this question is applied to a specific field when looking for empirical evidence.
• Null Hypothesis - This is used to contradict the expected effect of dependent and independent variables. 
• Alternative Hypothesis - Several hypotheses are given, but as the experiment proceeds, the alternative hypothesis is introduced to reflect the conditions of the experiment. 
• Logical Hypothesis - These hypotheses are able to be verified using logic.
• Statistical Hypothesis ​ - A hypothesis of this type is one that can be proven using statistical analysis.

For more information about how to create a hypothesis, have a look at the  Fundamentals of Research Methodology  by Engwa Godwill. 

Based on the hypothesis created, the researcher will need to determine the best research design for the experiment. 

Health Sciences Librarian

• << Previous: Initial Steps
• Next: Research Designs and Methods >>
• Last Updated: Jul 26, 2023 10:10 AM
• URL: https://libguides.apsu.edu/OriginalResearch

• Nebraska Medicine

Understanding Hypothesis Testing, Significance Level, Power and Sample Size Calculation

• Written by Steph Langel
• Published Apr 4, 2024

This e-module offers an in-depth discussion of the essential components of hypothesis testing: significance levels, statistical power, and sample size calculations, which are fundamental to rigorous research methodology. Learners will develop a comprehensive understanding of designing, interpreting, and evaluating research findings through interactive content and real-world case studies. This will enable them to make well-informed decisions based on statistical best practices. The module’s framework allows a thorough learning experience, starting from fundamental definitions and progressing to the hands-on implementation of statistical ideas. This ensures that learners acquire the essential abilities to conduct ethically appropriate and scientifically valid research.

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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

• How to Write a Hypothesis in 6 Steps - Grammarly
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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!

• Scientific Methods

What is Hypothesis?

We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.

A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

• The hypothesis should be clear and precise to consider it to be reliable.
• If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables.
• The hypothesis must be specific and should have scope for conducting more tests.
• The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance.

Sources of Hypothesis

Following are the sources of hypothesis:

• The resemblance between the phenomenon.
• Observations from past studies, present-day experiences and from the competitors.
• Scientific theories.
• General patterns that influence the thinking process of people.

Types of Hypothesis

There are six forms of hypothesis and they are:

• Simple hypothesis
• Complex hypothesis
• Directional hypothesis
• Non-directional hypothesis
• Null hypothesis
• Associative and casual hypothesis

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Null Hypothesis

It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.

Examples of Hypothesis

Following are the examples of hypotheses based on their types:

• Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis.
• All lilies have the same number of petals is an example of a null hypothesis.
• If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis.

Functions of Hypothesis

Following are the functions performed by the hypothesis:

• Hypothesis helps in making an observation and experiments possible.
• It becomes the start point for the investigation.
• Hypothesis helps in verifying the observations.
• It helps in directing the inquiries in the right direction.

How will Hypothesis help in the Scientific Method?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Formation of question
• Doing background research
• Creation of hypothesis
• Designing an experiment
• Collection of data
• Result analysis
• Summarizing the experiment
• Communicating the results

Frequently Asked Questions – FAQs

What is hypothesis.

A hypothesis is an assumption made based on some evidence.

Give an example of simple hypothesis?

What are the types of hypothesis.

Types of hypothesis are:

• Associative and Casual hypothesis

State true or false: Hypothesis is the initial point of any investigation that translates the research questions into a prediction.

Define complex hypothesis..

A complex hypothesis shows the relationship between two or more dependent variables and two or more independent variables.

Put your understanding of this concept to test by answering a few MCQs. Click ‘Start Quiz’ to begin!

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Computer Science > Machine Learning

Title: deep generative models through the lens of the manifold hypothesis: a survey and new connections.

Abstract: In recent years there has been increased interest in understanding the interplay between deep generative models (DGMs) and the manifold hypothesis. Research in this area focuses on understanding the reasons why commonly-used DGMs succeed or fail at learning distributions supported on unknown low-dimensional manifolds, as well as developing new models explicitly designed to account for manifold-supported data. This manifold lens provides both clarity as to why some DGMs (e.g. diffusion models and some generative adversarial networks) empirically surpass others (e.g. likelihood-based models such as variational autoencoders, normalizing flows, or energy-based models) at sample generation, and guidance for devising more performant DGMs. We carry out the first survey of DGMs viewed through this lens, making two novel contributions along the way. First, we formally establish that numerical instability of high-dimensional likelihoods is unavoidable when modelling low-dimensional data. We then show that DGMs on learned representations of autoencoders can be interpreted as approximately minimizing Wasserstein distance: this result, which applies to latent diffusion models, helps justify their outstanding empirical results. The manifold lens provides a rich perspective from which to understand DGMs, which we aim to make more accessible and widespread.

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The Hierarchy-of-Hypotheses Approach: A Synthesis Method for Enhancing Theory Development in Ecology and Evolution

Department of Biodiversity Research and Systematic Botany, University of Potsdam, Potsdam, Germany

Department of Restoration Ecology, Technical University of Munich, Freising, Germany

Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany

Carlos A Aguilar-Trigueros

Institute of Biology, Freie Universität, Berlin, Berlin, Germany

Isabelle Bartram

Institute of Sociology, University of Freiburg, Freiburg

Raul Rennó Braga

Universidade Federal do Paraná, Laboratório de Ecologia e Conservação, Curitiba, Brazil

Gregory P Dietl

Paleontological Research Institution and the Department of Earth and Atmospheric Sciences at Cornell University, Ithaca, New York

Martin Enders

Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany

David J Gibson

School of Biological Sciences, Southern Illinois University Carbondale, Carbondale, Illinois

Lorena Gómez-Aparicio

Instituto de Recursos Naturales y Agrobiología de Sevilla, CSIC, LINCGlobal, Sevilla, Spain

Pierre Gras

Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research (IZW), also in Berlin, Germany

Department of Conservation Biology, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany

Sophie Lokatis

Christopher j lortie.

Department of Biology, York University, York, Canada, as well as with the National Center for Ecological Analysis and Synthesis, University of California Santa Barbara, Santa Barbara, California

Anne-Christine Mupepele

Chair of Nature Conservation and Landscape Ecology, University of Freiburg, Freiburg, and the Senckenberg Biodiversity and Climate Research Centre, Frankfurt am Main, both in Germany

Stefan Schindler

Environment Agency Austria and University of Vienna's Division of Conservation, Biology, Vegetation, and Landscape Ecology, Vienna, Austria, and his third affiliation is with Community Ecology and Conservation, Czech University of Life Sciences Prague, Prague, Czech Republic, Finally

Jostein Starrfelt

University of Oslo's Centre for Ecological and Evolutionary Synthesis and with the Norwegian Scientific Committee for Food and Environment, Norwegian Institute of Public Health, both in Oslo, Norway

Alexis D Synodinos

Department of Plant Ecology and Nature Conservation, University of Potsdam, Potsdam, Germany

Centre for Biodiversity Theory and Modelling, Theoretical, and Experimental Ecology Station, CNRS, Moulis, France

Jonathan M Jeschke

Associated data.

In the current era of Big Data, existing synthesis tools such as formal meta-analyses are critical means to handle the deluge of information. However, there is a need for complementary tools that help to (a) organize evidence, (b) organize theory, and (c) closely connect evidence to theory. We present the hierarchy-of-hypotheses (HoH) approach to address these issues. In an HoH, hypotheses are conceptually and visually structured in a hierarchically nested way where the lower branches can be directly connected to empirical results. Used for organizing evidence, this tool allows researchers to conceptually connect empirical results derived through diverse approaches and to reveal under which circumstances hypotheses are applicable. Used for organizing theory, it allows researchers to uncover mechanistic components of hypotheses and previously neglected conceptual connections. In the present article, we offer guidance on how to build an HoH, provide examples from population and evolutionary biology and propose terminological clarifications.

In many disciplines, the volume of evidence published in scientific journals is steadily increasing. In principle, this increase should make it possible to describe and explain complex systems in much greater detail than ever before. However, an increase in available information does not necessarily correspond to an increase in knowledge and understanding (Jeschke et al. 2019 ). Publishing results in scientific journals and depositing data in public archives does not guarantee their practical application, reuse, or the advancement of theory. We suggest that this situation can be improved by the development, establishment, and regular application of methods that have the explicit aim of linking evidence and theory.

An important step toward more efficiently exploiting results from case studies is synthesis (for this and other key terms, see box 1 ). There is a wealth of methods available for statistically combining the results of multiple studies (Pullin et al. 2016 , Dicks et al. 2017 ). These methods enable the synthesis of research results stemming from different studies that address a common question (Koricheva et al. 2013 ). In the environmental sciences, evidence synthesis has increased both in frequency and importance (Lortie 2014 ), seeking to make empirical evidence readily available and more suitable as a basis for decision-making (e.g., evidence-based decision making; Sutherland 2006 , Diefenderfer et al. 2016 , Pullin et al. 2016 , Cook et al. 2017 , Dicks et al. 2017 ). Moreover, methodological guidelines have been developed, and web portals implemented to collect and synthesize the results of primary studies. Prime examples are the platforms www.conservationevidence.com and www.environmentalevidence.org , alongside the European Union–funded projects EKLIPSE ( www.eklipse-mechanism.eu ) and BiodiversityKnowledge (Nesshöver et al. 2016 ). These initiatives have promoted significant advances in the organization and assessment of evidence and the implementation of synthesis, thus allowing for a comprehensive representation of applied knowledge in environmental sciences.

Box 1. Glossary.

Evidence. Available body of data and information indicating whether a belief or proposition is true or valid (Howick 2011 , Mupepele et al. 2016 ). These data and information can, for example, stem from an empirical observation, model output, or simulation.

Hypothesis. An assumption that (a) is based on a formalized or nonformalized theoretical model of the real world and (b) can deliver one or more testable predictions (after Giere et al. 2005 ).

Mechanistic hypothesis . Narrowed version of an overarching hypothesis, resulting from specialization or decomposition of the unspecified hypothesis with respect to assumed underlying causes.

Operational hypothesis. Narrowed version of an overarching hypothesis, accounting for a specific study design. Operational hypotheses explicate which method (e.g., which study system or research approach) is used to study the overarching hypothesis.

Overarching hypothesis. Unspecified assumption derived from a general idea, concept or major principle (i.e., from a general ­theoretical model).

Prediction. Statement about how data (i.e., measured outcome of an experiment or observation) should look if the underlying hypothesis is true.

Synthesis. Process of identifying, compiling and combining relevant knowledge from multiple sources.

Theory. A high-level—that is, general—system of conceptual constructs or devices to explain and understand ecological, evolutionary or other phenomena and systems (adapted from Pickett et al. 2007 ). Theory can consist of a worked out, integrated body of mechanistic rules or even natural laws, but it may also consist of a loose collection of conceptual frameworks, ideas and hypotheses.

Fostering evidence-based decision-making is crucial to solving specific applied problems. However, findings resulting from these applied approaches for evidence synthesis are usually not reconnected to a broader body of theory. Therefore, they do not consistently contribute to a structured or targeted advancement of theory—for example, by assessing the usefulness of ideas. It is a missed opportunity to not feed this synthesized evidence back into theory. A similar lack of connection to theory has been observed for studies addressing basic research questions (e.g., Jeltsch et al. 2013 , Scheiner 2013 ). Evidence feeding back into theory, subsequently leading to further theory development, would become a more appealing, simpler and, therefore, more common process if there were well described and widely accepted methods. A positive example in this respect is structural equation modeling, especially if combined with metamodels (Grace et al. 2010 ). With this technique, theoretical knowledge directly feeds into mathematical models, and empirical data are then used to select the model best matching the observations.

In the present article, we provide a detailed description of a relatively new synthesis method—the hierarchy-of-hypotheses (HoH) approach (Jeschke et al. 2012 , Heger et al. 2013 )—that is complementary to existing knowledge synthesis tools. This approach offers the opportunity to organize evidence and ideas, and to create and display links between single study results and theory. We suggest that the representation of broad ideas as nested hierarchies of hypotheses can be powerful and can be used to more efficiently connect single studies to a body of theory. Empirical studies usually formulate very specific hypotheses, derive predictions from these about expected data, and test these predictions in experiments or observations. With an HoH, it can be made explicit which broader ideas these specific hypotheses are linked to. The specific hypotheses can be characterized and visualized as subhypotheses of a broader idea or theory. Therefore, it becomes clear that the single study, although necessarily limited in its scope, is testing an important aspect of a broader idea or theory. Similarly, an HoH can be used to organize a body of literature that is too heterogeneous for statistical meta-analysis. It can be linked with a systematic review of existing studies, so that the studies and their findings are organized and hierarchically structured, thus visualizing which aspects of an overarching question or hypothesis each study is addressing. Alternatively, the HoH approach can be used to refine a broad idea on theoretical grounds and to identify different possibilities of how an idea, concept, or hypothesis can become more specific, less ambiguous, and better structured. Taken together, the approach can help to strengthen the theoretical foundations of a research field.

In this context, it is important to clarify what is meant by hypothesis . In the present article, we apply the terminology offered by the philosopher of science Ronald Giere and colleagues (Giere et al. 2005 , see also Griesemer 2018 ). Accordingly, a hypothesis provides the connection of the (formalized or nonformalized) theoretical model that a researcher has, describing how a specific part of the world works in theory, to the real world by asserting that the model fits that part of the world in some specified aspect. A hypothesis needs to be testable, thus allowing the investigation of whether the theoretical model actually fits the real world. This is done by deriving one or more predictions from the hypothesis that state how data (gathered in an observation or experiment) should look if the hypothesis is true.

The HoH approach has already been introduced as a tool for synthesis in invasion ecology (Jeschke et al. 2012 , Heger et al. 2013 , Heger and Jeschke 2014 , Jeschke and Heger 2018a ). So far, however, explicit and consistent guidance on how to build a hierarchy of hypotheses has not been formally articulated. The primary objective of this publication therefore is to offer a concrete, consistent, and refined description for those who want to use this tool or want to adopt it to their discipline. Furthermore, we want to stimulate methodological discussions about its further development and improvement. In the following, we outline the main ideas behind the HoH approach and the history of its development, present a primer for creating HoHs, provide examples for applications within and outside of invasion ecology, and discuss its strengths and limitations.

The hierarchy-of-hypotheses approach

The basic tenet behind the HoH approach is that complexity can often be handled by hierarchically structuring the topic under study (Heger and Jeschke 2018c ). The approach has been developed to clarify the link between big ideas, and experiments or surveys designed to test them. Usually, experiments and surveys actually test predictions derived from smaller, more specific ideas that represent an aspect or one manifestation of the big idea. Different studies all addressing a joint major hypothesis consequently often each address different versions of it. This diversity makes it hard to reconcile their results. The HoH approach addresses this challenge by dividing the major hypothesis into more specific formulations or subhypotheses. These can be further divided until the level of refinement allows for direct empirical testing. The result is a tree that visually depicts different ways in which a major hypothesis can be formulated. The empirical studies can then be explicitly linked to the branch of the tree they intend to address, thus making a conceptual and visual connection to the major hypothesis. Hierarchical nestedness therefore allows one to structure and display relationships between different versions of an idea, and to conceptually collate empirical tests addressing the same overall question with divergent approaches. A hierarchical arrangement of hypotheses has also been suggested by Pickett and colleagues ( 2007 ) in the context of the method of pairwise alternative hypothesis testing (or strong inference, Platt 1964 ). However, we are not aware of studies that picked up on or further developed this idea.

The HoH approach in its first version (Jeschke et al. 2012 , Heger et al. 2013 , Heger and Jeschke 2014 ) was not a formalized method with a clear set of rules on how to proceed. It emerged and evolved during a literature synthesis project through dealing with the problem of how to merge results of a set of highly diverse studies without losing significant information on what precisely these studies were addressing. In that first iteration of the HoH method, the branches of the hierarchy were selected by the respective author team, on the basis of expert knowledge and assessment of published data. Therefore, pragmatic questions guided the creation of the HoH (e.g., which kind of branching helps group studies in a way that enhances interpretation? ). Through further work on the approach, helpful discussions with colleagues, and critical comments (Farji-Brener and Amador-Vargas 2018 , Griesemer 2018 , Scheiner and Fox 2018 ), suggestions for its refinement were formulated (Heger and Jeschke 2018b , 2018c ). The present article amounts to a further step in the methodological development and refinement of the HoH approach, including terminological clarifications and practical suggestions.

A primer for building a hierarchy of hypotheses

With the methodological guidance provided in the following, we take the initial steps toward formalizing the application of the HoH approach. However, we advocate that its usage should not be confined by rules that are too strict. Although we appreciate the advantages of strict methodological guidelines, such as those provided by The Collaboration for Environmental Evidence ( 2018 ) for synthesizing evidence in systematic reviews, we believe that when it comes to conceptual work and theory development, room is needed for creativity and methodological flexibility.

Applying the HoH approach involves four steps (figure  1 ). We distinguish two basic aims for creating an HoH: organizing evidence and organizing theory. These basic aims reflect the distinction between empirical and theoretical modeling approaches in Griesemer ( 2013 ). Creating and displaying links between evidence and theory can be part of the process in either case. In the first case (i.e., if the aim is to organize evidence), the process starts with a diverse set of empirical results and the question of how these can be grouped to enhance their joint interpretation or further analysis. In the second case (i.e., if the aim is to organize theory), the process of creating the hierarchy starts with decomposing an overarching hypothesis. An HoH allows one to make the meaning of this overarching hypothesis more explicit by formulating its components as separate subhypotheses from which testable, specific predictions can be derived.

Workflow for the creation of a hierarchy of hypotheses. For a detailed explanation, see the main text.

The starting point for an HoH-based analysis in both cases, for organizing evidence as well as for organizing theory, is the identification of a focal hypothesis. This starting point is followed by the compilation of information (step 1 in figure  1 ). Which information needs to be compiled depends on whether the aim is structuring and synthesizing empirical evidence provided by a set of studies (e.g., Jeschke and Heger 2018a and example 1 below) or whether the research interest is more in the theoretical structure and subdivision of the overarching hypothesis (see examples 2 and 3 below). The necessary information needs to be gathered by means of a literature review guided by expert knowledge. Especially if the aim is to organize evidence, we recommend applying a standardized procedure (e.g., PRISMA, Moher et al. 2015 , or ROSES, Haddaway et al. 2018 ) and recording the performed steps.

The next step is to create the hierarchy (step 2 in figure  1 ). If the aim is to organize evidence, step 1 will have led to the compilation of a set of studies empirically addressing the overarching hypothesis or a sufficiently homogeneous overarching theoretical framework. In step 2, these studies will need to be grouped. Depending on the aim of the study, it can be helpful to group the empirical tests of the overarching hypothesis according to study system (e.g., habitat, taxonomic group) or research approach (e.g., measured response variable). For example, in tests of the biotic resistance hypothesis in invasion ecology, which posits that an ecosystem with high biodiversity is more resistant against nonnative species than an ecosystem with lower biodiversity, Jeschke and colleagues ( 2018a ) grouped empirical tests according to how the tests measured biodiversity and resistance against nonnative species. Some tests measured biodiversity as species richness, others as evenness or functional richness. The groups resulting from such considerations can be interpreted as representing operational hypotheses, because they specify the general hypothesis by accounting for diverse research approaches—that is, options for measuring the hypothesized effect (see also Griesemer 2018 , Heger and Jeschke 2018c , as well as figure  2 a and example 1 below). In such cases, we recommend displaying all resulting subhypotheses, if feasible.

Three different types of branching in a hierarchy of hypotheses. The branching example shown in (a) is inspired by example 1 in the main text, (b) by example 2 (see also figure  3 b), and (c) by example 3 (see also figure  4 ).

If the aim is to organize theory, the overarching hypothesis is split into independent components on the basis of conceptual considerations (figure  2 b and  2 c). This splitting of the overarching hypothesis can be done by creating branches according to which factors could have caused the respective process or pattern (see example 2 below, figure  2 b).

Broad, overarching hypotheses often consist of several complementary partial arguments that are necessary elements. Consider the question why species often are well adapted to their biotic environment. A common hypothesis suggests that enduring interaction with enemies drives evolutionary changes, thus leading to adaptations of prey to their enemies (see example 3). This hypothesis presupposes that species face increasing risks from enemies but also that species’ traits evolve in response to the changed risk (figure  2 c and example 3 below). Decomposing overarching hypotheses into their partial arguments by formulating separate mechanistic hypotheses can enhance conceptual clarity and elucidate that sometimes, studies combined under one header are in fact addressing very different things.

For any type of branching, it is critical to identify components or groups (i.e., branches) that are mutually exclusive and not overlapping, so that an unambiguous assignment of single cases or observations into a box (i.e., subhypothesis) can be possible. If this is not feasible, it may be necessary to use conceptual maps, networks or Venn diagrams rather than hierarchical structures (figure  1 , step 2; also see supplemental table S1). Therefore, care should be taken not to impose a hierarchical structure in cases where it is not helpful.

For many applications, the process of building an HoH can stop at this step, and a publication of the results can be considered (step 4). The resulting HoH can, for example, show the connection of a planned study to a body of theory, explicate and visualize the complexity of ideas implicitly included in a major hypothesis, or develop a research program around an overarching idea.

If the aim is to identify research gaps, or to assess the generality or range of applicability of a major hypothesis, however, a further step must be taken (figure  1 , step 3a): The HoH needs to be linked to empirical data. In previous studies (e.g., Jeschke and Heger 2018a ), this step was done by assigning empirical studies to the subhypotheses they addressed and assessing the level of supporting evidence for the predictions derived from each hypothesis or subhypothesis. This assignment of studies to subhypotheses can be done either by using expert judgment or by applying machine learning algorithms (for further details, see Heger and Jeschke 2014 , Jeschke and Heger 2018a , Ryo et al. 2019 ). Depending on the research question, the available resources and the structure of the data, the level of evidence can be assessed for each subhypothesis as well as for the higher-level hypotheses and can then be compared across subhypotheses. Such a comparison can provide information on the generality of an overarching hypothesis (i.e., its unifying power and breadth of applicability) or on the range of conditions under which a mechanism applies (see supplemental table S2 for examples). Before an HoH organizing theory is connected to empirical evidence, it will be necessary in most cases to include operational hypotheses at the lower levels, specifying, for example, different possible experimental approaches.

The hierarchical approach can additionally be used to connect the HoH developed in step 2 to a related body of theory. For example, Heger and colleagues ( 2013 ) suggested that the existing HoH on the enemy release hypothesis (see example 1 below) was conceptually connected to another well-known hypothesis—the novel weapons hypothesis. As a common overarching hypothesis addressing the question why species can successfully establish and spread outside of their native range, they suggested the “lack of eco-evolutionary experience hypothesis”; the enemy release and the novel weapons hypotheses are considered subhypotheses of this overarching hypothesis. This optional step can therefore help to create missing links within a discipline or even across disciplines.

Performing this step requires the study of the related body of theory, looking for conceptual overlaps and overarching topics. It may turn out that hypotheses, concepts, and ideas exist that are conceptually linked to the focal overarching hypothesis but that these links are nonhierarchical. In these cases, it can be useful to build hypothesis networks and apply clustering techniques to identify underlying structures (see, e.g., Enders et al. 2020 ). This step can also be applied in cases in which the HoH has been built to organize evidence.

Once the HoH is finalized, it can be published in order to enter the public domain and facilitate the advancement of the methodology and theory development. For the future, we envision a platform for the publication of HoHs to make the structured representations of research topics available not only via the common path of journal publications. The webpage www.hi-knowledge.org (Jeschke et al. 2018b ) is a first step in this direction and is planned to allow for the upload of results in the future.

Application of the HoH approach: Three examples

We will now exemplify the process of creating an HoH. The first example starts with a diverse set of empirical tests addressing one overarching hypothesis (i.e., with the aim to organize evidence), whereas the second and third examples start with conceptual considerations on how different aspects are linked to one overarching hypothesis (in the present article, the aim is to organize theory).

Example 1: the enemy release hypothesis as a hierarchy

The first published study showing a detailed version of an HoH focused on the enemy release hypothesis (Heger and Jeschke 2014 ). This is a prominent hypothesis in invasion biology (Enders et al. 2018 ). With respect to the research question of why certain species become invasive—that is, why they establish and spread in a new range—it posits, “The absence of enemies is a cause of invasion success” (e.g., Keane and Crawley 2002 ). With a systematic literature review, Heger and Jeschke ( 2014 ) identified studies addressing this hypothesis. This review revealed that the hypothesis has been tested in many different ways. After screening the empirical tests with a specific focus on which research approach had been used, the authors decided to use three branching criteria: the indicator for enemy release (actual damage, infestation with enemies or performance of the invader); the type of comparison (alien versus natives, aliens in native versus invaded range or invasive versus noninvasive aliens); and the type of enemies (specialists or generalists). On the basis of these criteria, Heger and Jeschke created a hierarchically organized representation of the hypothesis's multiple aspects. The order in which the three criteria were applied to create the hierarchy in this case was based on practical considerations. Empirical studies providing evidence were then assigned to the respective branch of the corresponding hierarchy to reveal specific subhypotheses that were more and others that were less supported (Heger and Jeschke 2014 ).

In later publications, Heger and Jeschke suggested some optional refinements of the original approach (Heger and Jeschke 2018b , 2018c ). One of the suggestions was to distinguish between mechanistic hypotheses (originally termed working hypotheses) and operational hypotheses as different forms of subhypotheses when building the hierarchy. Mechanistic hypotheses serve the purpose of refining the broad, overarching idea in a conceptual sense (figure  2 b and  2 c), whereas operational hypotheses refine the hypotheses by accounting for the diversity of study approaches (figure  2 a).

The enemy release hypothesis example indicates that it can be useful to apply different types of branching criteria within one study. Heger and Jeschke ( 2014 ) looked for helpful ways of grouping diverse empirical tests. Some of the branches they decided to create were based on differences in the research methods, such as the distinction between comparisons of aliens versus natives, and comparisons of aliens in their native versus the invaded range (figure  2 a). Other branches explicate complementary partial arguments contained in the major hypothesis: Studies in which the researchers asked whether aliens are confronted with fewer enemies were separated from those in which they asked whether aliens that are released show enhanced performance.

In this example, the HoH approach was used to organize evidence and therefore to expose the variety of manifestations of the enemy release hypothesis and to display the level of evidence for each branch of the HoH (see Heger and Jeschke 2018b and supplemental table S2 for an interpretation of the results).

Example 2: illustrating the potential drivers of the snowshoe hare–canadian lynx population cycles

Understanding and predicting the spatiotemporal dynamics of populations is one of ecology's central goals (Sutherland et al. 2013 ), and population ecology has a long tradition of trying to understand causes for observed patterns in population dynamics. However, research efforts do not always produce clear conclusions, and often lead to competing explanatory hypotheses. A good example, which has been popularized through textbooks, is the 8–11-year synchronized population cycles of the snowshoe hare ( Lepus americanus ) and the Canadian lynx ( Lynx canadensis ; figure  3 a). From eighteenth- to nineteenth-century fur trapping records across the North American boreal and northern temperate forests, it has been known that predator (lynx) and prey (hare) exhibit broadly synchronous population cycles. Research since the late 1930s (MacLulich 1937 , Elton and Nicholson 1942 ) has tried to answer the question how these patterns are produced. A linear food chain of producer (vegetation)—primary consumer or prey (snowshoe hares)—secondary consumer or predator (Canadian lynx) proved too simplistic as an explanation (Stenseth et al. 1997 ). Instead, multiple drivers could have been responsible, resulting in the development of multiple competing explanations (Oli et al. 2020 ).

(a) The population cycle of snowshoe hare and Canadian lynx and (b) a hierarchy of hypotheses illustrating its potential drivers. The hypotheses (blue boxes) branch from the overarching hypothesis into more and more precise mechanistic hypotheses and are confronted with empirical tests (arrows leading to grey boxes) at lower levels of the hierarchy. The broken lines indicate where the hierarchy may be extended. Sources: The figure is based on the summary of snowshoe hare–Canadian lynx research (Krebs et al. 2001 , Krebs et al. 2018 and references therein). Panel (a) is reprinted with permission from OpenStax Biology, Chapter 45.6 Community Ecology, Rice University Publishers, Creative Commons Attribution License (by 4.0).

In the present article, we created an HoH to organize the current suggestions on what drives the snowshoe hare–lynx cycle (figure  3 b). The aim of this exercise is to visualize conceptual connections rooted in current population ecological theory and, therefore, to enhance understanding of the complexity of involved processes.

A major hypothesis in population ecology is that populations are regulated by the interaction between biotic and abiotic factors. This regulation can either happen through processes coupled with the density of the focal organisms (density-dependent processes) or through density-independent processes, such as variability in environmental conditions or disturbances. This conceptual distinction can be used to branch out multiple mechanistic hypotheses that specify particular hypothetical mechanisms inducing the observed cycles. For example, potential drivers of the hare–lynx cycles include density-dependent mechanisms linked to bottom-up resource limitation and top-down predation, and density-independent mechanisms related to 10-year sun spot cycles. Figure  3 b also summarizes the kind of experiments that have been performed and how they relate to the corresponding mechanistic hypotheses. For example, food supplementation and fertilization experiments were used to test the resource limitation hypothesis and predator exclusion experiments to test the hypothesis that hare cycles are induced by predator abundance. Figure  3 b therefore highlights why it can be useful to apply very different types of experiments to test one broad overarching hypothesis.

The experiments that have been performed suggest that the predator–prey cycles result from an interaction between predation and food supplies combined with other modifying factors including social stress, disease and parasitism (Krebs et al., 2001 , 2018 ). Other experiments can be envisioned to test additional hypotheses, such as snow-removal experiments to test whether an increase in winter snow, induced by changed sun spot activity, causes food shortages and high hare mortality (Krebs et al. 2018 ).

In this example, alternate hypotheses are visually contrasted, and the different experiments that have been done are linked to the nested structure of possible drivers. This allows one to intuitively grasp the conceptual contribution of evidence stemming from each experiment to the overall explanation of the pattern. In a next step, quantitative results from these experiments could be summarized and displayed as well—for example, applying formal meta-analyses to summarize and display evidence stemming from each type of experiment. This example highlights how hierarchically structuring hypotheses can help to visually organize ideas about which drivers potentially cause a pattern in a complex system (for a comparison, see figure 11 in Krebs et al. 2018 ).

Example 3: the escalation hypothesis of evolution

The escalation hypothesis is a prominent hypothesis in evolutionary biology. In response to the question why species often seem to be well adapted to their biotic environment, it states that enemies are predominant agents of natural selection, and that enduring interactions with enemies brings about long-term evolutionary trends in the morphology, behavior, and distribution of organisms. Escalation, however, is an intrinsically costly process that can proceed only as long as resources are both available and accessible. Since the publication of Vermeij's book Evolution and Escalation in 1987, which is usually considered the start of the respective modern research program, escalation has represented anything but a fixed theory in its structure or content. The growth of escalation studies has led to the development of an increasing number of specific subhypotheses derived from Vermeij's original formulation and therefore to an expansion of the theoretical domain of the escalation hypothesis. Escalation has been supported by some tests but questioned by others.

Similar as in example 2, an HoH can contribute to conceptual clarity by structuring the diversity of escalation ideas that have been proposed (figure  4 ; Dietl 2015 ). To create the HoH for the escalation hypothesis, instead of assembling empirical studies that have tested it, Dietl ( 2015 ) went through the conceptual exercise of arranging existing escalation ideas on the basis of expert knowledge.

A hierarchy of hypotheses for the escalation hypothesis in evolutionary biology. The broken lines indicate where the hierarchy may be extended.

In its most generalized formulation—that is, “enemies direct evolution”—the escalation hypothesis can be situated at the top of a branch (figure  4 ) along with other hypotheses positing the importance of interaction-related adaptation, such as Van Valen's ( 1973 ) Red Queen hypothesis and hypotheses derived from Thompson's ( 2005 ) geographic mosaic theory of coevolution. Vermeij's original ( 1987 ) formulation of the hypothesis of escalation is actually composed of two separate testable propositions: “Biological hazards due to competitors and predators have become more severe over the course of time in physically comparable habitats” ­(p. 49 in Vermeij 1987 ) and “traits that enhance the competitive and antipredatory capacities of individual organisms have increased in incidence and in degree of expression over the course of time within physically similar habitats” (p. 49 in Vermeij 1987 ). As is the case with other composite hypotheses, these ideas must be singled out before the overarching idea can be unambiguously tested. This requirement creates a natural branching point in the escalation HoH, the risk and response subhypotheses (figure  4 ).

Other lower-level hypotheses and aspects of the risk and response subhypotheses are possible. The risk side of the HoH can be further branched into subhypotheses suggesting either that the enemies evolved enhanced traits through time (e.g., allowing for greater effectiveness in prey capture) or that interaction intensity has increased through time (e.g., because of greater abundance or power of predators; figure  4 ). The response side of the HoH also can be further branched into several subhypotheses (all addressed by Vermeij 1987 ). In particular, species’ responses could take the form of a trend toward more rapid exploitation of resources through time, an increased emphasis on traits that enable individuals to combat or interfere with competitors, a trend toward reduced detectability of prey through time, a trend of increased mobility (that is, active escape defense) through time, or an increase in the development of armor (or passive defense) through time. Arranging these different options of how escalation can manifest in boxes connected to a hierarchical structure helps to gain an overview. The depiction of subhypotheses in separate boxes does not indicate that the authors believe there is no interaction possible among these factors. For example, the evolution of enhanced traits may lead to an increase in interaction intensity. The presented HoH should be viewed only as one way to organize theory. It puts emphasis on the upward connections of subhypotheses to more general hypotheses. If the focus is more on interactions among different factors, other graphical and conceptual approaches may be more helpful (e.g., causal networks; for an example, see Gurevitch et al. 2011 ).

The HoH shown in figure  4 can be used as a conceptual backbone for further work in this field. Also, it can be related to existing evidence. This HoH will allow identification of data gaps and an understanding of which branches of the tree receive support by empirical work and therefore should be considered important components of escalation theory.

Strengths and limits of the HoH approach

The HoH approach can help to organize theory, to organize evidence, and to conceptualize and visualize connections of evidence to theory. Previously published examples of HoHs (e.g., Jeschke and Heger 2018a ) and example 1 given above demonstrate its usefulness for organizing evidence, for pointing out important differences among subhypotheses and for conceptually and graphically connecting empirical results to a broader theoretical idea. Such an HoH can make the rationale underlying a specific study explicit and can elucidate the conceptual connection of the study to a concrete theoretical background.

Applying the HoH approach can also help disclose knowledge gaps and biases (Braga et al. 2018 ) and can help reveal which research approaches have been used to assess an overarching idea (for examples, see Jeschke and Heger 2018a ; other methods can be used to reach these aims too—e.g., systematic maps; Pullin et al. 2016 , Collaboration for Environmental Evidence 2018 ). On the basis of such information, future research can be focused on especially promising areas or methods.

Besides such descriptive applications, the HoH approach can be combined with evidence assessment techniques (step 3a in figure  1 ). It can help to analyze the level of evidence for subhypotheses and therefore deliver the basis for discussing their usefulness and range of applicability (table S2; Jeschke and Heger 2018). Recent studies demonstrate that this kind of application can be useful for research outside of ecology as well—for example, in biomedical research (Bartram and Jeschke 2019 ) or even in a distant field like company management research (Wu et al. 2019 ).

We did not detail in the present article how the confrontation of hypotheses with evidence in an HoH can be done, but in previous work it was shown that this step can deliver the basis for enhancing theory. For example, the HoH-based literature analyses presented in Jeschke and Heger ( 2018a ) showed that several major hypotheses in invasion biology are only weakly supported by evidence. The authors consequently suggested to reformulate them (Jeschke and Heger 2018b ) and to explicitly assess their range of applicability (Heger and Jeschke 2018a ). Because an HoH visually connects data and theory, the approach motivates one to feed empirical results back into theory and, therefore, use them for improving theory. It is our vision that in the future, theory development in ecology and evolution could largely profit from a regular application of the HoH approach. Steps to improve theory can include highlighting strongly supported subhypotheses, pointing out hypotheses with low unification power and breadth of applicability, shedding light on previously unnoticed connections, and revealing gaps in research.

The examples on the hare–lynx cycles and the escalation hypothesis showed that the HoH approach can also guide theory-driven reasoning in both the ecological and evolutionary domains, respectively. That is, the HoH approach can allow the reconsideration and reorganization of conceptual ideas without directly referring to data. Major hypotheses or research questions are usually composed of several elements, and above, we suggest how these elements can be exposed and visualized (figure  2 b and  2 c). In this way, applying the HoH approach can help to enhance conceptual clarity by displaying different meanings and components of broad concepts. Conceptual clarity is not only useful to avoid miscommunication or misinterpretation of empirical results, but we expect that it will also facilitate theory development by enhancing accurate thinking and argumentation.

In addition, the nested, hierarchical structure invites looking for connections upward: Figure  4 shows the escalation hypothesis as one variant of an even broader hypothesis, positing that “Species interactions direct evolution.” This in turn can enhance the future search for patterns and mechanisms across unconnected study fields. A respective example can be found in Schulz and colleagues ( 2019 ). In that article, the authors used the HoH approach to organize twelve hypotheses each addressing the roles that antagonists play during species invasions. By grouping the hypotheses in a hierarchically nested way, Schulz and colleagues showed their conceptual relatedness, which had not been demonstrated before.

In the future, the HoH approach could also be used for creating interdisciplinary links. There are many research questions that are being addressed in several research areas in parallel, using different approaches and addressing different aspects of the overall question. In an HoH, such connections could be revealed. Heger and colleagues ( 2019 ) suggested a future application of the HoH approach for organizing and structuring research on effects of global change on organisms, communities, and ecosystems. Under the broad header of “ecological novelty,” more specific research questions addressed in various disciplines (e.g., climate change research, biodiversity research, urban ecology, restoration ecology, evolutionary ecology, microbial ecology) could be organized and therefore conceptually connected.

Importantly, the HoH approach can be easily combined with existing synthesis tools. For example, as was outlined above and in figure  1 , a systematic literature review can be used to identify and structure primary studies to be used for building an HoH. Statistical approaches, such as machine learning, can be used to optimize branching with respect to levels of evidence (Ryo et al. 2019 ), and empirical data structured in an HoH can be analyzed with formal meta-analysis—for example, separately for each subhypothesis (Jeschke and Pyšek 2018 ). In future applications, an HoH could also be used to visualize the results of a research-weaving process, in which systematic mapping is combined with bibliometric approaches (Nakagawa et al. 2019 ). Furthermore, HoHs can be linked to a larger network. An example is the website https://hi-knowledge.org/invasion-biology/ (Jeschke et al. 2018b ) where the conceptual connections of 12 major hypotheses of invasion ecology are displayed as a hierarchical network. We believe that the combination of HoH with other knowledge synthesis tools, such as Venn diagrams, ontologies, controlled vocabularies, and systematic maps, can be useful as well and should be explored in the future.

We emphasize, however, that the HoH method is by far no panacea for managing complexity. Not all topics interesting for scientific inquiry can be organized hierarchically, and imposing a hierarchy may even lead to wrong conclusions, thus actually hindering theory development. For example, to focus a conceptual synthesis on one major overarching hypothesis may conceal that other factors not addressed by this single hypothesis have a major effect on underlying processes as well. Evidence assessed with respect to this one hypothesis can in such cases only be used to derive partial explanations, whereas for a more complete understanding of the underlying processes, interactions with other factors need to be considered. Furthermore, displaying interacting aspects of a system as discrete entities within a hierarchy can obfuscate the true dynamics of a system.

In our three examples—the enemy release hypothesis, the hare–lynx cycles, and the escalation hypothesis (figures  3 and  4 )—connections between the different levels of the hierarchies do not necessarily depict causal relationships. Also, the fact that multicausality is ubiquitous in ecological systems is not covered. It has been argued that approaches directly focusing on explicating causal relationships and multicausality could be more helpful for advancing theory (Scheiner and Fox 2018 ). The HoH approach is currently primarily a tool to provide conceptual structure. We suggest that revealing causal networks and multicausalities represent additional objectives and regard them as important aims also for further developing the HoH approach. Combining existing approaches for revealing causal relationships (e.g., Eco Evidence, Norris et al. 2012 , or CADDIS, www.epa.gov/caddis ) with the HoH approach seems to be a promising path forward. Also, a future aim could be to develop a version of the HoH approach with enhanced formalization, allowing different kinds of relationships among subhypotheses to be disclosed (e.g., applying semantic web methods. Such a formalized version of the HoH approach could be used for scrutinizing the logical structure of hypotheses (e.g., compatibility and incompatibility of subhypotheses) and identifying inevitable interdependencies (e.g., likelihood of cooccurrence of evidence along two branches).

The guidelines on how to build an HoH presented above and in figures  1 and  2 will help to increase the reproducibility of the process. Full reproducibility is unlikely to be reached for most applications because researchers need to make individual choices. For example, step 1 involves creative reasoning and may therefore potentially lead to differing results if repeated by different researchers. The process of creating an HoH can therefore lead to a whole set of outcomes. Usually, there will be not one single HoH that is the one “correct” answer to the research questions. Certain steps of the process can be automated using artificial intelligence, such as with the use of decision-tree algorithms to enhance reproducibility (Ryo et al. 2019 ). But even if such techniques are applied, the choice of which information is fed into the algorithms is made by a researcher. We suggest that this ambiguity should not be considered a flaw of the method, but instead an important and necessary concession to creativity, offering the chance to closely match the outcome of the process to the concrete requirements of the research project. Also, it should be noted that other approaches for knowledge synthesis do not necessarily yield reproducible results either, not even formal meta-analysis (de Vrieze 2018 ).

Conclusions

The current emphasis on statistical approaches for synthesizing evidence with the purpose of facilitating decision making in environmental management and nature conservation is undoubtedly important and necessary. However, knowledge and understanding of ecological systems would profit largely if results from empirical studies would in addition, and on a regular basis, be used to improve theory. With this contribution, we present one possibility for creating close links between evidence and theory, and we hope to stimulate future studies that feed results from case studies back into theory. Our goal is to motivate more conceptual work aimed at refining major hypotheses on how complex systems work. Above, we provided examples for how to develop a nuanced representation of major hypotheses, focusing on their mechanistic components.

Ecological systems are highly complex, and therefore, the theories describing them typically need to incorporate complexity. Nested, hierarchical structures in our view represent one possible path forward, because they allow zooming in and out and, therefore, moving between different levels of complexity. We propose that alternative tools such as causal networks should be further developed for application in ecology and evolution as well. Combining complementary conceptual tools would in our view be most promising for an efficient enhancement of knowledge and understanding in ecology.

Supplementary Material

Biaa130_supplemental_file, acknowledgments.

The ideas presented in this article were developed during the workshop “The hierarchy-of-hypotheses approach: Exploring its potential for structuring and analyzing theory, research, and evidence across disciplines,” 19–21 July 2017, and refined during the workshop “Research synthesis based on the hierarchy-of-hypotheses approach,” 10–12 October 2018, both in Hanover, Germany. We thank William Bausman, Adam Clark, Francesco DePrentis, Carsten Dormann, Alexandra Erfmeier, Gordon Fox, Jeremy Fox, James Griesemer, Volker Grimm, Thierry Hanser, Frank Havemann, Yuval Itescu, Marie Kaiser, Julia Koricheva, Peter Kraker, Ingolf Kühn, Andrew Latimer, Chunlong Liu, Bertram Ludäscher, Klaus Mainzer, Elijah Millgram, Bob O'Hara, Masahiro Ryo, Raphael Scholl, Gerhard Schurz, Philip Stephens, Koen van Benthem and Meike Wittman for participating in our lively discussions and Alkistis Elliot-Graves and Birgitta König-Ries for help with refining terminology. Furthermore, we thank Sam Scheiner and five anonymous reviewers for comments that helped to improve the manuscript. The workshops were funded by Volkswagen Foundation (Az 92,807 and 94,246). TH, CAA, ME, PG, ADS, and JMJ received funding from German Federal Ministry of Education and Research within the Collaborative Project “Bridging in Biodiversity Science” (grant no. 01LC1501A). ME additionally received funding from the Foundation of German Business, JMJ from the Deutsche Forschungsgemeinschaft (grants no. JE 288/9–1 and JE 288/9–2), and IB from German Federal Ministry of Education and Research (grant no. FKZ 01GP1710). CJL was supported by a grant from The Natural Sciences and Engineering Research Council of Canada and in-kind synthesis support from the US National Center for Ecological Analysis and Synthesis. LGA was supported by the Spanish Ministry of Science, Innovation, and Universities through project no. CGL2014–56,739-R, and RRB received funding from the Brazilian National Council for Scientific and Technological Development (process no. 152,289/2018–6).

Author Biographical

Tina Heger ( [email protected] ) is affiliated with the Department of Biodiversity Research and Systematic Botany and Alexis D. Synodinos is affiliated with the Department of Plant Ecology and Nature Conservation at the University of Potsdam, in Potsdam, Germany. Tina Heger and Kurt Jax are affiliated with the Department of Restoration Ecology at the Technical University of Munich, in Freising, Germany. Tina Heger, Carlos A. Aguilar-Trigueros, Martin Enders, Pierre Gras, Jonathan M. Jeschke, Sophie Lokatis, and Alexis Synodinos are affiliated with the ­Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), in Berlin, Germany. Carlos Aguilar, Isabelle Bartram, Martin Enders, Jonathan M. Jeschke, and Sophie Lokatis are affiliated with the Institute of Biology at Freie Universität Berlin, in Berlin, Germany. Martin Enders, Jonathan M. Jeschke, and Sophie Lokatis are also affiliated with the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), in Berlin, Germany. Pierre Gras is also affiliated with the Department of Ecological Dynamics at the Leibniz Institute for Zoo and Wildlife Research (IZW), also in Berlin, Germany. Isabelle Bartram is affiliated with the Institute of Sociology, at the University of Freiburg, in Freiburg. Kurt Jax is also affiliated with the Department of Conservation Biology at the Helmholtz Centre for Environmental Research—UFZ, in Leipzig, Germany. Raul R. Braga is located at the Universidade Federal do Paraná, Laboratório de Ecologia e Conservação, in Curitiba, Brazil. Gregory P. Dietl has two affiliations: the Paleontological Research Institution and the Department of Earth and Atmospheric Sciences at Cornell University, in Ithaca, New York. David J. Gibson is affiliated with the School of Biological Sciences at Southern Illinois University Carbondale, in Carbondale, Illinois. Lorena Gómez-Aparicio's affiliation is the Instituto de Recursos Naturales y Agrobiología de Sevilla, CSIC, LINCGlobal, in Sevilla, Spain. Christopher J. Lortie is affiliated with the Department of Biology at York University, in York, Canada, as well as with the National Center for Ecological Analysis and Synthesis, at the University of California Santa Barbara, in Santa Barbara, California. Anne-Christine Mupepele has two affiliations as well: the Chair of Nature Conservation and Landscape Ecology at the University of Freiburg, in Freiburg, and the Senckenberg Biodiversity and Climate Research Centre, in Frankfurt am Main, both in Germany. Stefan Schindler is working at the Environment Agency Austria and the University of Vienna's Division of Conservation Biology, Vegetation, and Landscape Ecology, in Vienna, Austria, and his third affiliation is with Community Ecology and Conservation, at the Czech University of Life Sciences Prague, in Prague, Czech Republic. Finally, Jostein Starrfelt is affiliated with the University of Oslo's Centre for Ecological and Evolutionary Synthesis and with the Norwegian Scientific Committee for Food and Environment, Norwegian Institute of Public Health, both in Oslo, Norway. Alexis D. Synodinos is affiliated with the Centre for Biodiversity Theory and Modelling, Theoretical, and Experimental Ecology Station, CNRS, in Moulis, France.

Contributor Information

Tina Heger, Department of Biodiversity Research and Systematic Botany, University of Potsdam, Potsdam, Germany. Department of Restoration Ecology, Technical University of Munich, Freising, Germany. Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany.

Carlos A Aguilar-Trigueros, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Institute of Biology, Freie Universität, Berlin, Berlin, Germany.

Isabelle Bartram, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Institute of Biology, Freie Universität, Berlin, Berlin, Germany. Institute of Sociology, University of Freiburg, Freiburg.

Raul Rennó Braga, Universidade Federal do Paraná, Laboratório de Ecologia e Conservação, Curitiba, Brazil.

Gregory P Dietl, Paleontological Research Institution and the Department of Earth and Atmospheric Sciences at Cornell University, Ithaca, New York.

Martin Enders, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Institute of Biology, Freie Universität, Berlin, Berlin, Germany. Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany.

David J Gibson, School of Biological Sciences, Southern Illinois University Carbondale, Carbondale, Illinois.

Lorena Gómez-Aparicio, Instituto de Recursos Naturales y Agrobiología de Sevilla, CSIC, LINCGlobal, Sevilla, Spain.

Pierre Gras, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research (IZW), also in Berlin, Germany.

Kurt Jax, Department of Restoration Ecology, Technical University of Munich, Freising, Germany. Department of Conservation Biology, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany.

Sophie Lokatis, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Institute of Biology, Freie Universität, Berlin, Berlin, Germany. Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany.

Christopher J Lortie, Department of Biology, York University, York, Canada, as well as with the National Center for Ecological Analysis and Synthesis, University of California Santa Barbara, Santa Barbara, California.

Anne-Christine Mupepele, Chair of Nature Conservation and Landscape Ecology, University of Freiburg, Freiburg, and the Senckenberg Biodiversity and Climate Research Centre, Frankfurt am Main, both in Germany.

Stefan Schindler, Environment Agency Austria and University of Vienna's Division of Conservation, Biology, Vegetation, and Landscape Ecology, Vienna, Austria, and his third affiliation is with Community Ecology and Conservation, Czech University of Life Sciences Prague, Prague, Czech Republic, Finally.

Jostein Starrfelt, University of Oslo's Centre for Ecological and Evolutionary Synthesis and with the Norwegian Scientific Committee for Food and Environment, Norwegian Institute of Public Health, both in Oslo, Norway.

Alexis D Synodinos, Department of Plant Ecology and Nature Conservation, University of Potsdam, Potsdam, Germany. Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Centre for Biodiversity Theory and Modelling, Theoretical, and Experimental Ecology Station, CNRS, Moulis, France.

Jonathan M Jeschke, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Institute of Biology, Freie Universität, Berlin, Berlin, Germany. Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany.

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