hypothesis biology synonym

Hypothesis n., plural: hypotheses [/haɪˈpɑːθəsɪs/] Definition: Testable scientific prediction

Table of Contents

What Is Hypothesis?

A scientific hypothesis is a foundational element of the scientific method . It’s a testable statement proposing a potential explanation for natural phenomena. The term hypothesis means “little theory” . A hypothesis is a short statement that can be tested and gives a possible reason for a phenomenon or a possible link between two variables . In the setting of scientific research, a hypothesis is a tentative explanation or statement that can be proven wrong and is used to guide experiments and empirical research.

It is an important part of the scientific method because it gives a basis for planning tests, gathering data, and judging evidence to see if it is true and could help us understand how natural things work. Several hypotheses can be tested in the real world, and the results of careful and systematic observation and analysis can be used to support, reject, or improve them.

Researchers and scientists often use the word hypothesis to refer to this educated guess . These hypotheses are firmly established based on scientific principles and the rigorous testing of new technology and experiments .

For example, in astrophysics, the Big Bang Theory is a working hypothesis that explains the origins of the universe and considers it as a natural phenomenon. It is among the most prominent scientific hypotheses in the field.

“The scientific method: steps, terms, and examples” by Scishow:

Biology definition: A hypothesis  is a supposition or tentative explanation for (a group of) phenomena, (a set of) facts, or a scientific inquiry that may be tested, verified or answered by further investigation or methodological experiment. It is like a scientific guess . It’s an idea or prediction that scientists make before they do experiments. They use it to guess what might happen and then test it to see if they were right. It’s like a smart guess that helps them learn new things. A scientific hypothesis that has been verified through scientific experiment and research may well be considered a scientific theory .

Etymology: The word “hypothesis” comes from the Greek word “hupothesis,” which means “a basis” or “a supposition.” It combines “hupo” (under) and “thesis” (placing). Synonym: proposition; assumption; conjecture; postulate Compare:   theory See also: null hypothesis

Characteristics Of Hypothesis

A useful hypothesis must have the following qualities:

It should never be written as a question.
You should be able to test it in the real world to see if it’s right or wrong.
It needs to be clear and exact.
It should list the factors that will be used to figure out the relationship.
It should only talk about one thing. You can make a theory in either a descriptive or form of relationship.
It shouldn’t go against any natural rule that everyone knows is true. Verification will be done well with the tools and methods that are available.
It should be written in as simple a way as possible so that everyone can understand it.
It must explain what happened to make an answer necessary.
It should be testable in a fair amount of time.
It shouldn’t say different things.

Sources Of Hypothesis

Sources of hypothesis are:

Patterns of similarity between the phenomenon under investigation and existing hypotheses.
Insights derived from prior research, concurrent observations, and insights from opposing perspectives.
The formulations are derived from accepted scientific theories and proposed by researchers.
In research, it’s essential to consider hypothesis as different subject areas may require various hypotheses (plural form of hypothesis). Researchers also establish a significance level to determine the strength of evidence supporting a hypothesis.
Individual cognitive processes also contribute to the formation of hypotheses.

One hypothesis is a tentative explanation for an observation or phenomenon. It is based on prior knowledge and understanding of the world, and it can be tested by gathering and analyzing data. Observed facts are the data that are collected to test a hypothesis. They can support or refute the hypothesis.

For example, the hypothesis that “eating more fruits and vegetables will improve your health” can be tested by gathering data on the health of people who eat different amounts of fruits and vegetables. If the people who eat more fruits and vegetables are healthier than those who eat less fruits and vegetables, then the hypothesis is supported.

Hypotheses are essential for scientific inquiry. They help scientists to focus their research, to design experiments, and to interpret their results. They are also essential for the development of scientific theories.

Types Of Hypothesis

In research, you typically encounter two types of hypothesis: the alternative hypothesis (which proposes a relationship between variables) and the null hypothesis (which suggests no relationship).

Simple Hypothesis

It illustrates the association between one dependent variable and one independent variable. For instance, if you consume more vegetables, you will lose weight more quickly. Here, increasing vegetable consumption is the independent variable, while weight loss is the dependent variable.

Complex Hypothesis

It exhibits the relationship between at least two dependent variables and at least two independent variables. Eating more vegetables and fruits results in weight loss, radiant skin, and a decreased risk of numerous diseases, including heart disease.

Directional Hypothesis

It shows that a researcher wants to reach a certain goal. The way the factors are related can also tell us about their nature. For example, four-year-old children who eat well over a time of five years have a higher IQ than children who don’t eat well. This shows what happened and how it happened.

Non-directional Hypothesis

When there is no theory involved, it is used. It is a statement that there is a connection between two variables, but it doesn’t say what that relationship is or which way it goes.

Null Hypothesis

It says something that goes against the theory. It’s a statement that says something is not true, and there is no link between the independent and dependent factors. “H 0 ” represents the null hypothesis.

Associative and Causal Hypothesis

When a change in one variable causes a change in the other variable, this is called the associative hypothesis . The causal hypothesis, on the other hand, says that there is a cause-and-effect relationship between two or more factors.

Examples Of Hypothesis

Examples of simple hypotheses:

Students who consume breakfast before taking a math test will have a better overall performance than students who do not consume breakfast.
Students who experience test anxiety before an English examination 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, is a statement that suggests that drivers who talk on the phone while driving are more likely to make mistakes.

Examples of a complex hypothesis:

Individuals who consume a lot of sugar and don’t get much exercise are at an increased risk of developing depression.
Younger people who are routinely exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces, according to a new study.
Increased levels of air pollution led to higher rates of respiratory illnesses, which in turn resulted in increased costs for healthcare for the affected communities.

Examples of Directional Hypothesis:

The crop yield will go up a lot if the amount of fertilizer is increased.
Patients who have surgery and are exposed to more stress will need more time to get better.
Increasing the frequency of brand advertising on social media will lead to a significant increase in brand awareness among the target audience.

Examples of Non-Directional Hypothesis (or Two-Tailed Hypothesis):

The test scores of two groups of students are very different from each other.
There is a link between gender and being happy at work.
There is a correlation between the amount of caffeine an individual consumes and the speed with which they react.

Examples of a null hypothesis:

Children who receive a new reading intervention will have scores that are different than students who do not receive the intervention.
The results of a memory recall test will not reveal any significant gap in performance between children and adults.
There is not a significant relationship between the number of hours spent playing video games and academic performance.

Examples of Associative Hypothesis:

There is a link between how many hours you spend studying and how well you do in school.
Drinking sugary drinks is bad for your health as a whole.
There is an association between socioeconomic status and access to quality healthcare services in urban neighborhoods.

Functions Of Hypothesis

The research issue can be understood better with the help of a hypothesis, which is why developing one is crucial. The following are some of the specific roles that a hypothesis plays: (Rashid, Apr 20, 2022)

A hypothesis gives a study a point of concentration. It enlightens us as to the specific characteristics of a study subject we need to look into.
It instructs us on what data to acquire as well as what data we should not collect, giving the study a focal point .
The development of a hypothesis improves objectivity since it enables the establishment of a focal point.
A hypothesis makes it possible for us to contribute to the development of the theory. Because of this, we are in a position to definitively determine what is true and what is untrue .

How will Hypothesis help in the Scientific Method?

The scientific method begins with observation and inquiry about the natural world when formulating research questions. Researchers can refine their observations and queries into specific, testable research questions with the aid of hypothesis. They provide an investigation with a focused starting point.
Hypothesis generate specific predictions regarding the expected outcomes of experiments or observations. These forecasts are founded on the researcher’s current knowledge of the subject. They elucidate what researchers anticipate observing if the hypothesis is true.
Hypothesis direct the design of experiments and data collection techniques. Researchers can use them to determine which variables to measure or manipulate, which data to obtain, and how to conduct systematic and controlled research.
Following the formulation of a hypothesis and the design of an experiment, researchers collect data through observation, measurement, or experimentation. The collected data is used to verify the hypothesis’s predictions.
Hypothesis establish the criteria for evaluating experiment results. The observed data are compared to the predictions generated by the hypothesis. This analysis helps determine whether empirical evidence supports or refutes the hypothesis.
The results of experiments or observations are used to derive conclusions regarding the hypothesis. If the data support the predictions, then the hypothesis is supported. If this is not the case, the hypothesis may be revised or rejected, leading to the formulation of new queries and hypothesis.
The scientific approach is iterative, resulting in new hypothesis and research issues from previous trials. This cycle of hypothesis generation, testing, and refining drives scientific progress.

Importance Of Hypothesis

Hypothesis are testable statements that enable scientists to determine if their predictions are accurate. This assessment is essential to the scientific method, which is based on empirical evidence.
Hypothesis serve as the foundation for designing experiments or data collection techniques. They can be used by researchers to develop protocols and procedures that will produce meaningful results.
Hypothesis hold scientists accountable for their assertions. They establish expectations for what the research should reveal and enable others to assess the validity of the findings.
Hypothesis aid in identifying the most important variables of a study. The variables can then be measured, manipulated, or analyzed to determine their relationships.
Hypothesis assist researchers in allocating their resources efficiently. They ensure that time, money, and effort are spent investigating specific concerns, as opposed to exploring random concepts.
Testing hypothesis contribute to the scientific body of knowledge. Whether or not a hypothesis is supported, the results contribute to our understanding of a phenomenon.
Hypothesis can result in the creation of theories. When supported by substantive evidence, hypothesis can serve as the foundation for larger theoretical frameworks that explain complex phenomena.
Beyond scientific research, hypothesis play a role in the solution of problems in a variety of domains. They enable professionals to make educated assumptions about the causes of problems and to devise solutions.

Research Hypotheses: Did you know that a hypothesis refers to an educated guess or prediction about the outcome of a research study?

It’s like a roadmap guiding researchers towards their destination of knowledge. Just like a compass points north, a well-crafted hypothesis points the way to valuable discoveries in the world of science and inquiry.

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Biology library

Course: biology library > unit 1, the scientific method.

Controlled experiments
The scientific method and experimental design

Introduction

Make an observation.
Ask a question.
Form a hypothesis , or testable explanation.
Make a prediction based on the hypothesis.
Test the prediction.
Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

Observation: the toaster won't toast.

2. Ask a question.

Question: Why won't my toaster toast?

3. Propose a hypothesis.

Hypothesis: Maybe the outlet is broken.

4. Make predictions.

Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

Test of prediction: Plug the toaster into a different outlet and try again.
If the toaster does toast, then the hypothesis is supported—likely correct.
If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

Iteration time!
If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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4.14: Experiments and Hypotheses

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Now we’ll focus on the methods of scientific inquiry. Science often involves making observations and developing hypotheses. Experiments and further observations are often used to test the hypotheses.

A scientific experiment is a carefully organized procedure in which the scientist intervenes in a system to change something, then observes the result of the change. Scientific inquiry often involves doing experiments, though not always. For example, a scientist studying the mating behaviors of ladybugs might begin with detailed observations of ladybugs mating in their natural habitats. While this research may not be experimental, it is scientific: it involves careful and verifiable observation of the natural world. The same scientist might then treat some of the ladybugs with a hormone hypothesized to trigger mating and observe whether these ladybugs mated sooner or more often than untreated ones. This would qualify as an experiment because the scientist is now making a change in the system and observing the effects.

Forming a Hypothesis

When conducting scientific experiments, researchers develop hypotheses to guide experimental design. A hypothesis is a suggested explanation that is both testable and falsifiable. You must be able to test your hypothesis, and it must be possible to prove your hypothesis true or false.

For example, Michael observes that maple trees lose their leaves in the fall. He might then propose a possible explanation for this observation: “cold weather causes maple trees to lose their leaves in the fall.” This statement is testable. He could grow maple trees in a warm enclosed environment such as a greenhouse and see if their leaves still dropped in the fall. The hypothesis is also falsifiable. If the leaves still dropped in the warm environment, then clearly temperature was not the main factor in causing maple leaves to drop in autumn.

In the Try It below, you can practice recognizing scientific hypotheses. As you consider each statement, try to think as a scientist would: can I test this hypothesis with observations or experiments? Is the statement falsifiable? If the answer to either of these questions is “no,” the statement is not a valid scientific hypothesis.

Practice Questions

Determine whether each following statement is a scientific hypothesis.

No. This statement is not testable or falsifiable.
No. This statement is not testable.
No. This statement is not falsifiable.
Yes. This statement is testable and falsifiable.

[reveal-answer q=”429550″] Show Answers [/reveal-answer] [hidden-answer a=”429550″]

d: Yes. This statement is testable and falsifiable. This could be tested with a number of different kinds of observations and experiments, and it is possible to gather evidence that indicates that air pollution is not linked with asthma.
a: No. This statement is not testable or falsifiable. “Bad thoughts and behaviors” are excessively vague and subjective variables that would be impossible to measure or agree upon in a reliable way. The statement might be “falsifiable” if you came up with a counterexample: a “wicked” place that was not punished by a natural disaster. But some would question whether the people in that place were really wicked, and others would continue to predict that a natural disaster was bound to strike that place at some point. There is no reason to suspect that people’s immoral behavior affects the weather unless you bring up the intervention of a supernatural being, making this idea even harder to test.

[/hidden-answer]

Testing a Vaccine

Let’s examine the scientific process by discussing an actual scientific experiment conducted by researchers at the University of Washington. These researchers investigated whether a vaccine may reduce the incidence of the human papillomavirus (HPV). The experimental process and results were published in an article titled, “ A controlled trial of a human papillomavirus type 16 vaccine .”

Preliminary observations made by the researchers who conducted the HPV experiment are listed below:

Human papillomavirus (HPV) is the most common sexually transmitted virus in the United States.
There are about 40 different types of HPV. A significant number of people that have HPV are unaware of it because many of these viruses cause no symptoms.
Some types of HPV can cause cervical cancer.
About 4,000 women a year die of cervical cancer in the United States.

Practice Question

Researchers have developed a potential vaccine against HPV and want to test it. What is the first testable hypothesis that the researchers should study?

HPV causes cervical cancer.
People should not have unprotected sex with many partners.
People who get the vaccine will not get HPV.
The HPV vaccine will protect people against cancer.

[reveal-answer q=”20917″] Show Answer [/reveal-answer] [hidden-answer a=”20917″]Hypothesis A is not the best choice because this information is already known from previous studies. Hypothesis B is not testable because scientific hypotheses are not value statements; they do not include judgments like “should,” “better than,” etc. Scientific evidence certainly might support this value judgment, but a hypothesis would take a different form: “Having unprotected sex with many partners increases a person’s risk for cervical cancer.” Before the researchers can test if the vaccine protects against cancer (hypothesis D), they want to test if it protects against the virus. This statement will make an excellent hypothesis for the next study. The researchers should first test hypothesis C—whether or not the new vaccine can prevent HPV.[/hidden-answer]

Experimental Design

You’ve successfully identified a hypothesis for the University of Washington’s study on HPV: People who get the HPV vaccine will not get HPV.

The next step is to design an experiment that will test this hypothesis. There are several important factors to consider when designing a scientific experiment. First, scientific experiments must have an experimental group. This is the group that receives the experimental treatment necessary to address the hypothesis.

The experimental group receives the vaccine, but how can we know if the vaccine made a difference? Many things may change HPV infection rates in a group of people over time. To clearly show that the vaccine was effective in helping the experimental group, we need to include in our study an otherwise similar control group that does not get the treatment. We can then compare the two groups and determine if the vaccine made a difference. The control group shows us what happens in the absence of the factor under study.

However, the control group cannot get “nothing.” Instead, the control group often receives a placebo. A placebo is a procedure that has no expected therapeutic effect—such as giving a person a sugar pill or a shot containing only plain saline solution with no drug. Scientific studies have shown that the “placebo effect” can alter experimental results because when individuals are told that they are or are not being treated, this knowledge can alter their actions or their emotions, which can then alter the results of the experiment.

Moreover, if the doctor knows which group a patient is in, this can also influence the results of the experiment. Without saying so directly, the doctor may show—through body language or other subtle cues—his or her views about whether the patient is likely to get well. These errors can then alter the patient’s experience and change the results of the experiment. Therefore, many clinical studies are “double blind.” In these studies, neither the doctor nor the patient knows which group the patient is in until all experimental results have been collected.

Both placebo treatments and double-blind procedures are designed to prevent bias. Bias is any systematic error that makes a particular experimental outcome more or less likely. Errors can happen in any experiment: people make mistakes in measurement, instruments fail, computer glitches can alter data. But most such errors are random and don’t favor one outcome over another. Patients’ belief in a treatment can make it more likely to appear to “work.” Placebos and double-blind procedures are used to level the playing field so that both groups of study subjects are treated equally and share similar beliefs about their treatment.

The scientists who are researching the effectiveness of the HPV vaccine will test their hypothesis by separating 2,392 young women into two groups: the control group and the experimental group. Answer the following questions about these two groups.

This group is given a placebo.
This group is deliberately infected with HPV.
This group is given nothing.
This group is given the HPV vaccine.

[reveal-answer q=”918962″] Show Answers [/reveal-answer] [hidden-answer a=”918962″]

a: This group is given a placebo. A placebo will be a shot, just like the HPV vaccine, but it will have no active ingredient. It may change peoples’ thinking or behavior to have such a shot given to them, but it will not stimulate the immune systems of the subjects in the same way as predicted for the vaccine itself.
d: This group is given the HPV vaccine. The experimental group will receive the HPV vaccine and researchers will then be able to see if it works, when compared to the control group.

Experimental Variables

A variable is a characteristic of a subject (in this case, of a person in the study) that can vary over time or among individuals. Sometimes a variable takes the form of a category, such as male or female; often a variable can be measured precisely, such as body height. Ideally, only one variable is different between the control group and the experimental group in a scientific experiment. Otherwise, the researchers will not be able to determine which variable caused any differences seen in the results. For example, imagine that the people in the control group were, on average, much more sexually active than the people in the experimental group. If, at the end of the experiment, the control group had a higher rate of HPV infection, could you confidently determine why? Maybe the experimental subjects were protected by the vaccine, but maybe they were protected by their low level of sexual contact.

To avoid this situation, experimenters make sure that their subject groups are as similar as possible in all variables except for the variable that is being tested in the experiment. This variable, or factor, will be deliberately changed in the experimental group. The one variable that is different between the two groups is called the independent variable. An independent variable is known or hypothesized to cause some outcome. Imagine an educational researcher investigating the effectiveness of a new teaching strategy in a classroom. The experimental group receives the new teaching strategy, while the control group receives the traditional strategy. It is the teaching strategy that is the independent variable in this scenario. In an experiment, the independent variable is the variable that the scientist deliberately changes or imposes on the subjects.

Dependent variables are known or hypothesized consequences; they are the effects that result from changes or differences in an independent variable. In an experiment, the dependent variables are those that the scientist measures before, during, and particularly at the end of the experiment to see if they have changed as expected. The dependent variable must be stated so that it is clear how it will be observed or measured. Rather than comparing “learning” among students (which is a vague and difficult to measure concept), an educational researcher might choose to compare test scores, which are very specific and easy to measure.

In any real-world example, many, many variables MIGHT affect the outcome of an experiment, yet only one or a few independent variables can be tested. Other variables must be kept as similar as possible between the study groups and are called control variables . For our educational research example, if the control group consisted only of people between the ages of 18 and 20 and the experimental group contained people between the ages of 30 and 35, we would not know if it was the teaching strategy or the students’ ages that played a larger role in the results. To avoid this problem, a good study will be set up so that each group contains students with a similar age profile. In a well-designed educational research study, student age will be a controlled variable, along with other possibly important factors like gender, past educational achievement, and pre-existing knowledge of the subject area.

What is the independent variable in this experiment?

Sex (all of the subjects will be female)
Presence or absence of the HPV vaccine
Presence or absence of HPV (the virus)

[reveal-answer q=”68680″]Show Answer[/reveal-answer] [hidden-answer a=”68680″]Answer b. Presence or absence of the HPV vaccine. This is the variable that is different between the control and the experimental groups. All the subjects in this study are female, so this variable is the same in all groups. In a well-designed study, the two groups will be of similar age. The presence or absence of the virus is what the researchers will measure at the end of the experiment. Ideally the two groups will both be HPV-free at the start of the experiment.

List three control variables other than age.

[practice-area rows=”3″][/practice-area] [reveal-answer q=”903121″]Show Answer[/reveal-answer] [hidden-answer a=”903121″]Some possible control variables would be: general health of the women, sexual activity, lifestyle, diet, socioeconomic status, etc.

What is the dependent variable in this experiment?

Sex (male or female)
Rates of HPV infection
Age (years)

[reveal-answer q=”907103″]Show Answer[/reveal-answer] [hidden-answer a=”907103″]Answer b. Rates of HPV infection. The researchers will measure how many individuals got infected with HPV after a given period of time.[/hidden-answer]

Contributors and Attributions

Revision and adaptation. Authored by : Shelli Carter and Lumen Learning. Provided by : Lumen Learning. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
Scientific Inquiry. Provided by : Open Learning Initiative. Located at : https://oli.cmu.edu/jcourse/workbook/activity/page?context=434a5c2680020ca6017c03488572e0f8 . Project : Introduction to Biology (Open + Free). License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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noun as in theory

Strongest matches

explanation
interpretation
proposition
supposition

Strong matches

attribution
demonstration
presupposition
speculation

Weak matches

shot in the dark
starting point
tentative law

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Related words.

Words related to hypothesis are not direct synonyms, but are associated with the word hypothesis . Browse related words to learn more about word associations.

noun as in taking something for granted; something expected

expectation
postulation
presumption
sneaking suspicion
theorization

noun as in putting regard in as true

understanding

noun as in something regarded as true

fundamental
gospel truth

noun as in idea

abstraction
apprehension
conceptualization
consideration
fool notion
intellection

Viewing 5 / 29 related words

Example Sentences

Each one is a set of questions we’re fascinated by and hypotheses we’re testing.

Mousa’s research hinges on the “contact hypothesis,” the idea that positive interactions among rival group members can reduce prejudices.

Do more research on it, come up with a hypothesis as to why it underperforms, and try to improve it.

Now is the time to test your hypotheses to figure out what’s changing in your customers’ worlds, and address these topics directly.

Whether computing power alone is enough to fuel continued machine learning breakthroughs is a source of debate, but it seems clear we’ll be able to test the hypothesis.

Though researchers have struggled to understand exactly what contributes to this gender difference, Dr. Rohan has one hypothesis.

The leading hypothesis for the ultimate source of the Ebola virus, and where it retreats in between outbreaks, lies in bats.

In 1996, John Paul II called the Big Bang theory “more than a hypothesis.”

To be clear: There have been no double-blind or controlled studies that conclusively confirm this hair-loss hypothesis.

The bacteria-driven-ritual hypothesis ignores the huge diversity of reasons that could push someone to perform a religious ritual.

And remember it is by our hypothesis the best possible form and arrangement of that lesson.

Taken in connection with what we know of the nebulæ, the proof of Laplace's nebular hypothesis may fairly be regarded as complete.

What has become of the letter from M. de St. Mars, said to have been discovered some years ago, confirming this last hypothesis?

To admit that there had really been any communication between the dead man and the living one is also an hypothesis.

"I consider it highly probable," asserted Aunt Maria, forgetting her Scandinavian hypothesis.

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On this page you'll find 80 synonyms, antonyms, and words related to hypothesis, such as: assumption, axiom, conclusion, conjecture, explanation, and guess.

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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

proposition
supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

counter - hypothesis
Whorfian hypothesis
planetesimal hypothesis
nebular hypothesis
null hypothesis

Articles Related to hypothesis

This is the Difference Between a...

This is the Difference Between a Hypothesis and a Theory

In scientific reasoning, they're two completely different things

Dictionary Entries Near hypothesis

hypothermia

hypothesize

Cite this Entry

“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 17 Apr. 2024.

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What is a scientific hypothesis?

It's the initial building block in the scientific method.

A girl looks at plants in a test tube for a science experiment. What's her scientific hypothesis?

Hypothesis basics

What makes a hypothesis testable.

Types of hypotheses
Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research.

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

Elementary-age students study alternative energy using homemade windmills during public school science class.

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (BCcampus, 2015).

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley .

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts."

Read more about writing a hypothesis, from the American Medical Writers Association.
Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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Methodology

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.

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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Definition of hypothesis noun from the Oxford Advanced American Dictionary

formulate/advance a theory/hypothesis
build/construct/create/develop a simple/theoretical/mathematical model
develop/establish/provide/use a theoretical/conceptual framework/an algorithm
advance/argue/develop the thesis that…
explore an idea/a concept/a hypothesis
make a prediction/an inference
base a prediction/your calculations on something
investigate/evaluate/accept/challenge/reject a theory/hypothesis/model
design an experiment/a questionnaire/a study/a test
do research/an experiment/an analysis
make observations/calculations
take/record measurements
carry out/conduct/perform an experiment/a test/a longitudinal study/observations/clinical trials
run an experiment/a simulation/clinical trials
repeat an experiment/a test/an analysis
replicate a study/the results/the findings
observe/study/examine/investigate/assess a pattern/a process/a behavior
fund/support the research/project/study
seek/provide/get/secure funding for research
collect/gather/extract data/information
yield data/evidence/similar findings/the same results
analyze/examine the data/soil samples/a specimen
consider/compare/interpret the results/findings
fit the data/model
confirm/support/verify a prediction/a hypothesis/the results/the findings
prove a conjecture/hypothesis/theorem
draw/make/reach the same conclusions
read/review the records/literature
describe/report an experiment/a study
present/publish/summarize the results/findings
present/publish/read/review/cite a paper in a scientific journal

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Look up any word in the dictionary offline, anytime, anywhere with the Oxford Advanced Learner’s Dictionary app.

What Is a Hypothesis? (Science)

If...,Then...

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Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
B.A., Physics and Mathematics, Hastings College

A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject.

In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.

In the study of logic, a hypothesis is an if-then proposition, typically written in the form, "If X , then Y ."

In common usage, a hypothesis is simply a proposed explanation or prediction, which may or may not be tested.

Writing a Hypothesis

Most scientific hypotheses are proposed in the if-then format because it's easy to design an experiment to see whether or not a cause and effect relationship exists between the independent variable and the dependent variable . The hypothesis is written as a prediction of the outcome of the experiment.

Null Hypothesis and Alternative Hypothesis

Statistically, it's easier to show there is no relationship between two variables than to support their connection. So, scientists often propose the null hypothesis . The null hypothesis assumes changing the independent variable will have no effect on the dependent variable.

In contrast, the alternative hypothesis suggests changing the independent variable will have an effect on the dependent variable. Designing an experiment to test this hypothesis can be trickier because there are many ways to state an alternative hypothesis.

For example, consider a possible relationship between getting a good night's sleep and getting good grades. The null hypothesis might be stated: "The number of hours of sleep students get is unrelated to their grades" or "There is no correlation between hours of sleep and grades."

An experiment to test this hypothesis might involve collecting data, recording average hours of sleep for each student and grades. If a student who gets eight hours of sleep generally does better than students who get four hours of sleep or 10 hours of sleep, the hypothesis might be rejected.

But the alternative hypothesis is harder to propose and test. The most general statement would be: "The amount of sleep students get affects their grades." The hypothesis might also be stated as "If you get more sleep, your grades will improve" or "Students who get nine hours of sleep have better grades than those who get more or less sleep."

In an experiment, you can collect the same data, but the statistical analysis is less likely to give you a high confidence limit.

Usually, a scientist starts out with the null hypothesis. From there, it may be possible to propose and test an alternative hypothesis, to narrow down the relationship between the variables.

Example of a Hypothesis

Examples of a hypothesis include:

If you drop a rock and a feather, (then) they will fall at the same rate.
Plants need sunlight in order to live. (if sunlight, then life)
Eating sugar gives you energy. (if sugar, then energy)
White, Jay D. Research in Public Administration . Conn., 1998.
Schick, Theodore, and Lewis Vaughn. How to Think about Weird Things: Critical Thinking for a New Age . McGraw-Hill Higher Education, 2002.
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Biology Hypothesis

Delve into the fascinating world of biology with our definitive guide on crafting impeccable hypothesis thesis statements . As the foundation of any impactful biological research, a well-formed hypothesis paves the way for groundbreaking discoveries and insights. Whether you’re examining cellular behavior or large-scale ecosystems, mastering the art of the thesis statement is crucial. Embark on this enlightening journey with us, as we provide stellar examples and invaluable writing advice tailored for budding biologists.

What is a good hypothesis in biology?

A good hypothesis in biology is a statement that offers a tentative explanation for a biological phenomenon, based on prior knowledge or observation. It should be:

Testable: The hypothesis should be measurable and can be proven false through experiments or observations.
Clear: It should be stated clearly and without ambiguity.
Based on Knowledge: A solid hypothesis often stems from existing knowledge or literature in the field.
Specific: It should clearly define the variables being tested and the expected outcomes.
Falsifiable: It’s essential that a hypothesis can be disproven. This means there should be a possible result that could indicate the hypothesis is incorrect.

What is an example of a hypothesis statement in biology?

Example: “If a plant is given a higher concentration of carbon dioxide, then it will undergo photosynthesis at an increased rate compared to a plant given a standard concentration of carbon dioxide.”

In this example:

The independent variable (what’s being changed) is the concentration of carbon dioxide.
The dependent variable (what’s being measured) is the rate of photosynthesis. The statement proposes a cause-and-effect relationship that can be tested through experimentation.

100 Biology Thesis Statement Examples

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Biology, as the study of life and living organisms, is vast and diverse. Crafting a good thesis statement in this field requires a clear understanding of the topic at hand, capturing the essence of the research aim. From genetics to ecology, from cell biology to animal behavior, the following examples will give you a comprehensive idea about forming succinct biology thesis statements.

Genetics: Understanding the role of the BRCA1 gene in breast cancer susceptibility can lead to targeted treatments.

2. Evolution: The finch populations of the Galápagos Islands provide evidence of natural selection through beak variations in response to food availability.

3. Cell Biology: Mitochondrial dysfunction is a central factor in the onset of age-related neurodegenerative diseases.

4. Ecology: Deforestation in the Amazon directly impacts global carbon dioxide levels, influencing climate change.

5. Human Anatomy: Regular exercise enhances cardiovascular health by improving heart muscle function and reducing arterial plaque.

6. Marine Biology: Coral bleaching events in the Great Barrier Reef correlate strongly with rising sea temperatures.

7. Zoology: Migration patterns of Monarch butterflies are influenced by seasonal changes and available food sources.

8. Botany: The symbiotic relationship between mycorrhizal fungi and plant roots enhances nutrient absorption in poor soil conditions.

9. Microbiology: The overuse of antibiotics in healthcare has accelerated the evolution of antibiotic-resistant bacterial strains.

10. Physiology: High altitude adaptation in certain human populations has led to increased hemoglobin production.

11. Immunology: The role of T-cells in the human immune response is critical in developing effective vaccines against viral diseases.

12. Behavioral Biology: Birdsong variations in sparrows can be attributed to both genetic factors and environmental influences.

13. Developmental Biology: The presence of certain hormones during fetal development dictates the differentiation of sex organs in mammals.

14. Conservation Biology: The rapid decline of bee populations worldwide is directly linked to the use of certain pesticides in agriculture.

15. Molecular Biology: The CRISPR-Cas9 system has revolutionized gene editing techniques, offering potential cures for genetic diseases.

16. Virology: The mutation rate of the influenza virus necessitates annual updates in vaccine formulations.

17. Neurobiology: Neural plasticity in the adult brain can be enhanced through consistent learning and cognitive challenges.

18. Ethology: Elephant herds exhibit complex social structures and matriarchal leadership.

19. Biotechnology: Genetically modified crops can improve yield and resistance but also pose ecological challenges.

20. Environmental Biology: Industrial pollution in freshwater systems disrupts aquatic life and can lead to loss of biodiversity.

21. Neurodegenerative Diseases: Amyloid-beta protein accumulation in the brain is a key marker for Alzheimer’s disease progression.

22. Endocrinology: The disruption of thyroid hormone balance leads to metabolic disorders and weight fluctuations.

23. Bioinformatics: Machine learning algorithms can predict protein structures with high accuracy, advancing drug design.

24. Plant Physiology: The stomatal closure mechanism in plants helps prevent water loss and maintain turgor pressure.

25. Parasitology: The lifecycle of the malaria parasite involves complex interactions between humans and mosquitoes.

26. Molecular Genetics: Epigenetic modifications play a crucial role in gene expression regulation and cell differentiation.

27. Evolutionary Psychology: Human preference for symmetrical faces is a result of evolutionarily advantageous traits.

28. Ecosystem Dynamics: The reintroduction of apex predators in ecosystems restores ecological balance and biodiversity.

29. Epigenetics: Maternal dietary choices during pregnancy can influence the epigenetic profiles of offspring.

30. Biochemistry: Enzyme kinetics in metabolic pathways reveal insights into cellular energy production.

31. Bioluminescence: The role of bioluminescence in deep-sea organisms serves as camouflage and communication.

32. Genetics of Disease: Mutations in the CFTR gene cause cystic fibrosis, leading to severe respiratory and digestive issues.

33. Reproductive Biology: The influence of pheromones on mate selection is a critical aspect of reproductive success in many species.

34. Plant-Microbe Interactions: Rhizobium bacteria facilitate nitrogen fixation in leguminous plants, benefiting both organisms.

35. Comparative Anatomy: Homologous structures in different species provide evidence of shared evolutionary ancestry.

36. Stem Cell Research: Induced pluripotent stem cells hold immense potential for regenerative medicine and disease modeling.

37. Bioethics: Balancing the use of genetic modification in humans with ethical considerations is a complex challenge.

38. Molecular Evolution: The study of orthologous and paralogous genes offers insights into evolutionary relationships.

39. Bioenergetics: ATP synthesis through oxidative phosphorylation is a fundamental process driving cellular energy production.

40. Population Genetics: The Hardy-Weinberg equilibrium model helps predict allele frequencies in populations over time.

41. Animal Communication: The complex vocalizations of whales serve both social bonding and long-distance communication purposes.

42. Biogeography: The distribution of marsupials in Australia and their absence elsewhere highlights the impact of geographical isolation on evolution.

43. Aquatic Ecology: The phenomenon of eutrophication in lakes is driven by excessive nutrient runoff and results in harmful algal blooms.

44. Insect Behavior: The waggle dance of honeybees conveys precise information about the location of food sources to other members of the hive.

45. Microbial Ecology: The gut microbiome’s composition influences host health, metabolism, and immune system development.

46. Evolution of Sex: The Red Queen hypothesis explains the evolution of sexual reproduction as a defense against rapidly evolving parasites.

47. Immunotherapy: Manipulating the immune response to target cancer cells shows promise as an effective cancer treatment strategy.

48. Epigenetic Inheritance: Epigenetic modifications can be passed down through generations, impacting traits and disease susceptibility.

49. Comparative Genomics: Comparing the genomes of different species sheds light on genetic adaptations and evolutionary divergence.

50. Neurotransmission: The dopamine reward pathway in the brain is implicated in addiction and motivation-related behaviors.

51. Microbial Biotechnology: Genetically engineered bacteria can produce valuable compounds like insulin, revolutionizing pharmaceutical production.

52. Bioinformatics: DNA sequence analysis reveals evolutionary relationships between species and uncovers hidden genetic information.

53. Animal Migration: The navigational abilities of migratory birds are influenced by magnetic fields and celestial cues.

54. Human Evolution: The discovery of ancient hominin fossils provides insights into the evolutionary timeline of our species.

55. Cancer Genetics: Mutations in tumor suppressor genes contribute to the uncontrolled growth and division of cancer cells.

56. Aquatic Biomes: Coral reefs, rainforests of the sea, host incredible biodiversity and face threats from climate change and pollution.

57. Genomic Medicine: Personalized treatments based on an individual’s genetic makeup hold promise for more effective healthcare.

58. Molecular Pharmacology: Understanding receptor-ligand interactions aids in the development of targeted drugs for specific diseases.

59. Biodiversity Conservation: Preserving habitat diversity is crucial to maintaining ecosystems and preventing species extinction.

60. Evolutionary Developmental Biology: Comparing embryonic development across species reveals shared genetic pathways and evolutionary constraints.

61. Plant Reproductive Strategies: Understanding the trade-offs between asexual and sexual reproduction in plants sheds light on their evolutionary success.

62. Parasite-Host Interactions: The coevolution of parasites and their hosts drives adaptations and counter-adaptations over time.

63. Genomic Diversity: Exploring genetic variations within populations helps uncover disease susceptibilities and evolutionary history.

64. Ecological Succession: Studying the process of ecosystem recovery after disturbances provides insights into resilience and stability.

65. Conservation Genetics: Genetic diversity assessment aids in formulating effective conservation strategies for endangered species.

66. Neuroplasticity and Learning: Investigating how the brain adapts through synaptic changes improves our understanding of memory and learning.

67. Synthetic Biology: Designing and engineering biological systems offers innovative solutions for medical, environmental, and industrial challenges.

68. Ethnobotany: Documenting the traditional uses of plants by indigenous communities informs both conservation and pharmaceutical research.

69. Ecological Niche Theory: Exploring how species adapt to specific ecological niches enhances our grasp of biodiversity patterns.

70. Ecosystem Services: Quantifying the benefits provided by ecosystems, like pollination and carbon sequestration, supports conservation efforts.

71. Fungal Biology: Investigating mycorrhizal relationships between fungi and plants illuminates nutrient exchange mechanisms.

72. Molecular Clock Hypothesis: Genetic mutations accumulate over time, providing a method to estimate evolutionary divergence dates.

73. Developmental Disorders: Unraveling the genetic and environmental factors contributing to developmental disorders informs therapeutic approaches.

74. Epigenetics and Disease: Epigenetic modifications contribute to the development of diseases like cancer, diabetes, and neurodegenerative disorders.

75. Animal Cognition: Studying cognitive abilities in animals unveils their problem-solving skills, social dynamics, and sensory perceptions.

76. Microbiota-Brain Axis: The gut-brain connection suggests a bidirectional communication pathway influencing mental health and behavior.

77. Neurological Disorders: Neurodegenerative diseases like Parkinson’s and Alzheimer’s have genetic and environmental components that drive their progression.

78. Plant Defense Mechanisms: Investigating how plants ward off pests and pathogens informs sustainable agricultural practices.

79. Conservation Genomics: Genetic data aids in identifying distinct populations and prioritizing conservation efforts for at-risk species.

80. Reproductive Strategies: Comparing reproductive methods in different species provides insights into evolutionary trade-offs and reproductive success.

81. Epigenetics in Aging: Exploring epigenetic changes in the aging process offers insights into longevity and age-related diseases.

82. Antimicrobial Resistance: Understanding the genetic mechanisms behind bacterial resistance to antibiotics informs strategies to combat the global health threat.

83. Plant-Animal Interactions: Investigating mutualistic relationships between plants and pollinators showcases the delicate balance of ecosystems.

84. Adaptations to Extreme Environments: Studying extremophiles reveals the remarkable ways organisms thrive in extreme conditions like deep-sea hydrothermal vents.

85. Genetic Disorders: Genetic mutations underlie numerous disorders like cystic fibrosis, sickle cell anemia, and muscular dystrophy.

86. Conservation Behavior: Analyzing the behavioral ecology of endangered species informs habitat preservation and restoration efforts.

87. Neuroplasticity in Rehabilitation: Harnessing the brain’s ability to rewire itself offers promising avenues for post-injury or post-stroke rehabilitation.

88. Disease Vectors: Understanding how mosquitoes transmit diseases like malaria and Zika virus is critical for disease prevention strategies.

89. Biochemical Pathways: Mapping metabolic pathways in cells provides insights into disease development and potential therapeutic targets.

90. Invasive Species Impact: Examining the effects of invasive species on native ecosystems guides management strategies to mitigate their impact.

91. Molecular Immunology: Studying the intricate immune response mechanisms aids in the development of vaccines and immunotherapies.

92. Plant-Microbe Symbiosis: Investigating how plants form partnerships with beneficial microbes enhances crop productivity and sustainability.

93. Cancer Immunotherapy: Harnessing the immune system to target and eliminate cancer cells offers new avenues for cancer treatment.

94. Evolution of Flight: Analyzing the adaptations leading to the development of flight in birds and insects sheds light on evolutionary innovation.

95. Genomic Diversity in Human Populations: Exploring genetic variations among different human populations informs ancestry, migration, and susceptibility to diseases.

96. Hormonal Regulation: Understanding the role of hormones in growth, reproduction, and homeostasis provides insights into physiological processes.

97. Conservation Genetics in Plant Conservation: Genetic diversity assessment helps guide efforts to conserve rare and endangered plant species.

98. Neuronal Communication: Investigating neurotransmitter systems and synaptic transmission enhances our comprehension of brain function.

99. Microbial Biogeography: Mapping the distribution of microorganisms across ecosystems aids in understanding their ecological roles and interactions.

100. Gene Therapy: Developing methods to replace or repair defective genes offers potential treatments for genetic disorders.

Scientific Hypothesis Statement Examples

This section offers diverse examples of scientific hypothesis statements that cover a range of biological topics. Each example briefly describes the subject matter and the potential implications of the hypothesis.

Genetic Mutations and Disease: Certain genetic mutations lead to increased susceptibility to autoimmune disorders, providing insights into potential treatment strategies.
Microplastics in Aquatic Ecosystems: Elevated microplastic levels disrupt aquatic food chains, affecting biodiversity and human health through bioaccumulation.
Bacterial Quorum Sensing: Inhibition of quorum sensing in pathogenic bacteria demonstrates a potential avenue for novel antimicrobial therapies.
Climate Change and Phenology: Rising temperatures alter flowering times in plants, impacting pollinator interactions and ecosystem dynamics.
Neuroplasticity and Learning: The brain’s adaptability facilitates learning through synaptic modifications, elucidating educational strategies for improved cognition.
CRISPR-Cas9 in Agriculture: CRISPR-engineered crops with enhanced pest resistance showcase a sustainable approach to improving agricultural productivity.
Invasive Species Impact on Predators: The introduction of invasive prey disrupts predator-prey relationships, triggering cascading effects in terrestrial ecosystems.
Microbial Contributions to Soil Health: Beneficial soil microbes enhance nutrient availability and plant growth, promoting sustainable agriculture practices.
Marine Protected Areas: Examining the effectiveness of marine protected areas reveals their role in preserving biodiversity and restoring marine ecosystems.
Epigenetic Regulation of Cancer: Epigenetic modifications play a pivotal role in cancer development, highlighting potential therapeutic targets for precision medicine.

Testable Hypothesis Statement Examples in Biology

Testability hypothesis is a critical aspect of a hypothesis. These examples are formulated in a way that allows them to be tested through experiments or observations. They focus on cause-and-effect relationships that can be verified or refuted.

Impact of Light Intensity on Plant Growth: Increasing light intensity accelerates photosynthesis rates and enhances overall plant growth.
Effect of Temperature on Enzyme Activity: Higher temperatures accelerate enzyme activity up to an optimal point, beyond which denaturation occurs.
Microbial Diversity in Soil pH Gradients: Soil pH influences microbial composition, with acidic soils favoring certain bacterial taxa over others.
Predation Impact on Prey Behavior: The presence of predators induces changes in prey behavior, resulting in altered foraging strategies and vigilance levels.
Chemical Communication in Marine Organisms: Investigating chemical cues reveals the role of allelopathy in competition among marine organisms.
Social Hierarchy in Animal Groups: Observing animal groups establishes a correlation between social rank and access to resources within the group.
Effect of Habitat Fragmentation on Pollinator Diversity: Fragmented habitats reduce pollinator species richness, affecting plant reproductive success.
Dietary Effects on Gut Microbiota Composition: Dietary shifts influence gut microbiota diversity and metabolic functions, impacting host health.
Hybridization Impact on Plant Fitness: Hybrid plants exhibit varied fitness levels depending on the combination of parent species.
Human Impact on Coral Bleaching: Analyzing coral reefs under different anthropogenic stresses identifies the main factors driving coral bleaching events.

Scientific Investigation Hypothesis Statement Examples in Biology

This section emphasizes hypotheses that are part of broader scientific investigations. They involve studying complex interactions or phenomena and often contribute to our understanding of larger biological systems.

Genomic Variation in Human Disease Susceptibility: Genetic analysis identifies variations associated with increased risk of common diseases, aiding personalized medicine.
Behavioral Responses to Temperature Shifts in Insects: Investigating insect responses to temperature fluctuations reveals adaptation strategies to climate change.
Endocrine Disruptors and Amphibian Development: Experimental exposure to endocrine disruptors elucidates their role in amphibian developmental abnormalities.
Microbial Succession in Decomposition: Tracking microbial communities during decomposition uncovers the succession patterns of different decomposer species.
Gene Expression Patterns in Stress Response: Studying gene expression profiles unveils the molecular mechanisms underlying stress responses in plants.
Effect of Urbanization on Bird Song Patterns: Urban noise pollution influences bird song frequency and complexity, impacting communication and mate attraction.
Nutrient Availability and Algal Blooms: Investigating nutrient loading in aquatic systems sheds light on factors triggering harmful algal blooms.
Host-Parasite Coevolution: Analyzing genetic changes in hosts and parasites over time uncovers coevolutionary arms races and adaptation.
Ecosystem Productivity and Biodiversity: Linking ecosystem productivity to biodiversity patterns reveals the role of species interactions in ecosystem stability.
Habitat Preference of Invasive Species: Studying the habitat selection of invasive species identifies factors promoting their establishment and spread.

Hypothesis Statement Examples in Biology Research

These examples are tailored for research hypothesis studies. They highlight hypotheses that drive focused research questions, often leading to specific experimental designs and data collection methods.

Microbial Community Structure in Human Gut: Investigating microbial diversity and composition unveils the role of gut microbiota in human health.
Plant-Pollinator Mutualisms: Hypothesizing reciprocal benefits in plant-pollinator interactions highlights the role of coevolution in shaping ecosystems.
Chemical Defense Mechanisms in Insects: Predicting the correlation between insect feeding behavior and chemical defenses explores natural selection pressures.
Evolutionary Significance of Mimicry: Examining mimicry in organisms demonstrates its adaptive value in predator-prey relationships and survival.
Neurological Basis of Mate Choice: Proposing neural mechanisms underlying mate choice behaviors uncovers the role of sensory cues in reproductive success.
Mycorrhizal Symbiosis Impact on Plant Growth: Investigating mycorrhizal colonization effects on plant biomass addresses nutrient exchange dynamics.
Social Learning in Primates: Formulating a hypothesis on primate social learning explores the transmission of knowledge and cultural behaviors.
Effect of Pollution on Fish Behavior: Anticipating altered behaviors due to pollution exposure highlights ecological consequences on aquatic ecosystems.
Coevolution of Flowers and Pollinators: Hypothesizing mutual adaptations between flowers and pollinators reveals intricate ecological relationships.
Genetic Basis of Disease Resistance in Plants: Identifying genetic markers associated with disease resistance enhances crop breeding programs.

Prediction Hypothesis Statement Examples in Biology

Predictive simple hypothesis involve making educated guesses about how variables might interact or behave under specific conditions. These examples showcase hypotheses that anticipate outcomes based on existing knowledge.

Pesticide Impact on Insect Abundance: Predicting decreased insect populations due to pesticide application underscores ecological ramifications.
Climate Change and Migratory Bird Patterns: Anticipating shifts in migratory routes of birds due to climate change informs conservation strategies.
Ocean Acidification Effect on Coral Calcification: Predicting reduced coral calcification rates due to ocean acidification unveils threats to coral reefs.
Disease Spread in Crowded Bird Roosts: Predicting accelerated disease transmission in densely populated bird roosts highlights disease ecology dynamics.
Eutrophication Impact on Freshwater Biodiversity: Anticipating decreased freshwater biodiversity due to eutrophication emphasizes conservation efforts.
Herbivore Impact on Plant Species Diversity: Predicting reduced plant diversity in areas with high herbivore pressure elucidates ecosystem dynamics.
Predator-Prey Population Cycles: Predicting cyclical fluctuations in predator and prey populations showcases the role of trophic interactions.
Climate Change and Plant Phenology: Anticipating earlier flowering times due to climate change demonstrates the influence of temperature on plant life cycles.
Antibiotic Resistance in Bacterial Communities: Predicting increased antibiotic resistance due to overuse forewarns the need for responsible antibiotic use.
Human Impact on Avian Nesting Success: Predicting decreased avian nesting success due to habitat fragmentation highlights conservation priorities.

How to Write a Biology Hypothesis – Step by Step Guide

A hypothesis in biology is a critical component of scientific research that proposes an explanation for a specific biological phenomenon. Writing a well-formulated hypothesis sets the foundation for conducting experiments, making observations, and drawing meaningful conclusions. Follow this step-by-step guide to create a strong biology hypothesis:

1. Identify the Phenomenon: Clearly define the biological phenomenon you intend to study. This could be a question, a pattern, an observation, or a problem in the field of biology.

2. Conduct Background Research: Before formulating a hypothesis, gather relevant information from scientific literature. Understand the existing knowledge about the topic to ensure your hypothesis builds upon previous research.

3. State the Independent and Dependent Variables: Identify the variables involved in the phenomenon. The independent variable is what you manipulate or change, while the dependent variable is what you measure as a result of the changes.

4. Formulate a Testable Question: Based on your background research, create a specific and testable question that addresses the relationship between the variables. This question will guide the formulation of your hypothesis.

5. Craft the Hypothesis: A hypothesis should be a clear and concise statement that predicts the outcome of your experiment or observation. It should propose a cause-and-effect relationship between the independent and dependent variables.

6. Use the “If-Then” Structure: Formulate your hypothesis using the “if-then” structure. The “if” part states the independent variable and the condition you’re manipulating, while the “then” part predicts the outcome for the dependent variable.

7. Make it Falsifiable: A good hypothesis should be testable and capable of being proven false. There should be a way to gather data that either supports or contradicts the hypothesis.

8. Be Specific and Precise: Avoid vague language and ensure that your hypothesis is specific and precise. Clearly define the variables and the expected relationship between them.

9. Revise and Refine: Once you’ve formulated your hypothesis, review it to ensure it accurately reflects your research question and variables. Revise as needed to make it more concise and focused.

10. Seek Feedback: Share your hypothesis with peers, mentors, or colleagues to get feedback. Constructive input can help you refine your hypothesis further.

Tips for Writing a Biology Hypothesis Statement

Writing a biology alternative hypothesis statement requires precision and clarity to ensure that your research is well-structured and testable. Here are some valuable tips to help you create effective and scientifically sound hypothesis statements:

1. Be Clear and Concise: Your hypothesis statement should convey your idea succinctly. Avoid unnecessary jargon or complex language that might confuse your audience.

2. Address Cause and Effect: A hypothesis suggests a cause-and-effect relationship between variables. Clearly state how changes in the independent variable are expected to affect the dependent variable.

3. Use Specific Language: Define your variables precisely. Use specific terms to describe the independent and dependent variables, as well as any conditions or measurements.

4. Follow the “If-Then” Structure: Use the classic “if-then” structure to frame your hypothesis. State the independent variable (if) and the expected outcome (then). This format clarifies the relationship you’re investigating.

5. Make it Testable: Your hypothesis must be capable of being tested through experimentation or observation. Ensure that there is a measurable and observable way to determine if it’s true or false.

6. Avoid Ambiguity: Eliminate vague terms that can be interpreted in multiple ways. Be precise in your language to avoid confusion.

7. Base it on Existing Knowledge: Ground your hypothesis in prior research or existing scientific theories. It should build upon established knowledge and contribute new insights.

8. Predict a Direction: Your hypothesis should predict a specific outcome. Whether you anticipate an increase, decrease, or a difference, your hypothesis should make a clear prediction.

9. Be Focused: Keep your hypothesis statement focused on one specific idea or relationship. Avoid trying to address too many variables or concepts in a single statement.

10. Consider Alternative Explanations: Acknowledge alternative explanations for your observations or outcomes. This demonstrates critical thinking and a thorough understanding of your field.

11. Avoid Value Judgments: Refrain from including value judgments or opinions in your hypothesis. Stick to objective and measurable factors.

12. Be Realistic: Ensure that your hypothesis is plausible and feasible. It should align with what is known about the topic and be achievable within the scope of your research.

13. Refine and Revise: Draft multiple versions of your hypothesis statement and refine them. Discuss and seek feedback from mentors, peers, or advisors to enhance its clarity and precision.

14. Align with Research Goals: Your hypothesis should align with the overall goals of your research project. Make sure it addresses the specific question or problem you’re investigating.

15. Be Open to Revision: As you conduct research and gather data, be open to revising your hypothesis if the evidence suggests a different outcome than initially predicted.

Remember, a well-crafted biology science hypothesis statement serves as the foundation of your research and guides your experimental design and data analysis. It’s essential to invest time and effort in formulating a clear, focused, and testable hypothesis that contributes to the advancement of scientific knowledge.

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Hypothesis Examples

A hypothesis is a prediction of the outcome of a test. It forms the basis for designing an experiment in the scientific method . A good hypothesis is testable, meaning it makes a prediction you can check with observation or experimentation. Here are different hypothesis examples.

Null Hypothesis Examples

The null hypothesis (H 0 ) is also known as the zero-difference or no-difference hypothesis. It predicts that changing one variable ( independent variable ) will have no effect on the variable being measured ( dependent variable ). Here are null hypothesis examples:

Plant growth is unaffected by temperature.
If you increase temperature, then solubility of salt will increase.
Incidence of skin cancer is unrelated to ultraviolet light exposure.
All brands of light bulb last equally long.
Cats have no preference for the color of cat food.
All daisies have the same number of petals.

Sometimes the null hypothesis shows there is a suspected correlation between two variables. For example, if you think plant growth is affected by temperature, you state the null hypothesis: “Plant growth is not affected by temperature.” Why do you do this, rather than say “If you change temperature, plant growth will be affected”? The answer is because it’s easier applying a statistical test that shows, with a high level of confidence, a null hypothesis is correct or incorrect.

Research Hypothesis Examples

A research hypothesis (H 1 ) is a type of hypothesis used to design an experiment. This type of hypothesis is often written as an if-then statement because it’s easy identifying the independent and dependent variables and seeing how one affects the other. If-then statements explore cause and effect. In other cases, the hypothesis shows a correlation between two variables. Here are some research hypothesis examples:

If you leave the lights on, then it takes longer for people to fall asleep.
If you refrigerate apples, they last longer before going bad.
If you keep the curtains closed, then you need less electricity to heat or cool the house (the electric bill is lower).
If you leave a bucket of water uncovered, then it evaporates more quickly.
Goldfish lose their color if they are not exposed to light.
Workers who take vacations are more productive than those who never take time off.

Is It Okay to Disprove a Hypothesis?

Yes! You may even choose to write your hypothesis in such a way that it can be disproved because it’s easier to prove a statement is wrong than to prove it is right. In other cases, if your prediction is incorrect, that doesn’t mean the science is bad. Revising a hypothesis is common. It demonstrates you learned something you did not know before you conducted the experiment.

Test yourself with a Scientific Method Quiz .

Mellenbergh, G.J. (2008). Chapter 8: Research designs: Testing of research hypotheses. In H.J. Adèr & G.J. Mellenbergh (eds.), Advising on Research Methods: A Consultant’s Companion . Huizen, The Netherlands: Johannes van Kessel Publishing.
Popper, Karl R. (1959). The Logic of Scientific Discovery . Hutchinson & Co. ISBN 3-1614-8410-X.
Schick, Theodore; Vaughn, Lewis (2002). How to think about weird things: critical thinking for a New Age . Boston: McGraw-Hill Higher Education. ISBN 0-7674-2048-9.
Tobi, Hilde; Kampen, Jarl K. (2018). “Research design: the methodology for interdisciplinary research framework”. Quality & Quantity . 52 (3): 1209–1225. doi: 10.1007/s11135-017-0513-8

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What is a Hypothesis?

Hypothesis means something taken or supposed for granted, with the object of following out its consequences. In Greek, the term hypothesis is “a putting under,” and in Latin, it is equivalent to being suppositio.

Scientific Hypothesis

In the plan of an action course, one may consider different alternatives, working out each in a detailed way. Although the term hypothesis is typically not used in this particular case, this procedure is virtually similar to that of an investigator of crime considering different suspects. Various methods can be used for deciding what the different alternatives may be, but the fundamental is that the consideration of a supposal as if it were true, without actually accepting it to be true. The earliest use of the word in this sense was present in geometry, which is described by Plato in the Meno.

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The essential modern use of a hypothesis is in relation to scientific investigation. Merely, a scientist is not concerned about accumulating such facts as may be discovered by observation: linkages should be discovered to connect such facts. An initial problem or puzzle provides the impetus, but clues should be used to ascertain which facts will help in yielding a solution.

The tentative hypothesis is the best guide that fits within the existing doctrine body. With its help, it is so framed that deductions may be made that under certain factual conditions (also called “initial conditions”), certain other facts would be found when, if the hypothesis were correct.

Concept of Hypothesis

The concepts that are involved in the hypothesis need not themselves refer to the observable objects. But, the initial conditions must be capable of being observed or produced experimentally, and the deduced facts must be able to be observed. The research made by William Harvey on circulation in animals demonstrates how greatly the experimental observation is helped by a fruitful hypothesis. While a hypothesis may be confirmed partially by showing what is deduced from it with certain initial conditions is found under those conditions actually, it can’t be completely proved in this way.

What would have to be shown is, no other hypothesis would serve. Therefore, in assessing the hypothesis soundness, stress is laid on the variety and range of facts that can be brought under its scope. Also, it is essential that it should be capable of being linked systematically with the hypotheses that have been found fertile in other fields.

Predictions

If the predictions, which are derived from the hypothesis are not found to be true, the hypothesis may have to be modified or given up. However, the fault may lie in some other principle, which forms part of the body of accepted doctrine that has been utilized in deducing hypothesis consequences. Also, it may lie in the fact that other conditions, hitherto have unobserved, are present beside the initial conditions, affecting the entire result.

Therefore, the hypothesis can be kept pending further examination of some remodelling of principles or facts. A good illustration of this can be found in the history of the undulatory and the corpuscular hypotheses about light.

Working Hypothesis

A working hypothesis is one that is tentatively accepted as a foundation for more study in the hopes of producing a tenable theory, even though the hypothesis fails in the end. Similar to all hypotheses, a working hypothesis can be constructed as a statement of expectations that can be linked to the exploratory research purpose in the empirical investigation. Often, working hypotheses may be used as a conceptual framework in qualitative researches.

The working hypotheses’ provisional nature makes them useful as an organizing device in any applied research. Here, they act as a useful guide to address the problems, which are still in a formative phase.

Uses of Hypothesis

The theory was originally referred to as a plot outline of a classical drama. The word hypothesis in English comes from the ancient Greek word, whose literal or etymological hypothesis sense is about "placing or putting under" and thus, in extended use, has several other meanings, including "supposition."

Socrates deconstructs virtue in Plato's Meno (86e–87b) using a mathematical approach known as "investigating from a hypothesis." In this particular sense, 'hypothesis' refers to a convenient mathematical approach or a clever idea that simplifies the cumbersome calculations. And Cardinal Bellarmine gave one of the famous hypothesis examples about this usage in the warning issued to Galileo in the early 17th century: that he should not treat the Earth’s motion as a reality but merely as a hypothesis.

In the 21st century’s common usage, a hypothesis term refers to a provisional idea whose merit needs evaluation. For a proper evaluation, the hypothesis’ framer needs to describe specifics in operational terms. A hypothesis needs more work by the researcher to either disprove or confirm it. In due course, a confirmed hypothesis can occasionally grow to become a theory itself or become part of a theory.

FAQs on Hypothesis

1. Explain the Scientific Hypothesis?

Answer: In general, scientific hypotheses contain the form of a mathematical model. At times, but not always, we can also formulate them as existential statements, stating that some specific phenomenon instances under the examination contain some causal explanations and characteristics, which contain the general form of universal statements, stating that each and every phenomenon instance has a particular characteristic.

2. Explain the Importance of the Hypothesis?

Answer: Hempel's deductive-nomological hypothesis model concepts play an important role in both the development and testing of hypotheses. Many formal hypotheses connect the concepts by specifying the relationships that are expected between the propositions. When hypotheses are grouped together, they become a conceptual framework type. And, when a conceptual framework incorporates explanation or causality and complexity, it is generally called a theory.

3. What is Statistical Hypothesis Testing?

Answer: Whenever a possible correlation or any similar relation between the phenomena is investigated, like whether a proposed remedy is effective in the treatment of a disease, the hypothesis, which a relation exists cannot be examined the similar way one might examine any proposed new law of nature. In that type of investigation, if the tested remedy exhibits no effect in some cases, these do not necessarily falsify the concept of hypothesis.

4. How is Statistical Testing Used?

Answer: Statistical tests may be used to define how likely it is that the entire effect would be noticed if the hypothesized relation doesn’t exist. If that particular likelihood is sufficiently small (for example, less than 1%), the relation's existence may be assumed. Else, any observed effect can be due to pure chance.

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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.

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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.

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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 ).

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(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.

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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.

Bartram I, Jeschke JM. 2019. Do cancer stem cells exist? A pilot study combining a systematic review with the hierarchy-of-hypotheses approach . PLOS ONE 14 : e0225898. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Braga RR, Gómez-Aparicio L, Heger T, Vitule JRS, Jeschke JM. 2018. Structuring evidence for invasional meltdown: Broad support but with biases and gaps . Biological Invasions 20 : 923–936. [ Google Scholar ]
Collaboration for Environmental Evidence . 2018. Guidelines and Standards for Evidence Synthesis in Environmental Management, version 5.0 . Collaboration for Environmental Evidence . www.environmentalevidence.org/information-for-authors . [ Google Scholar ]
Cook CN, Nichols SJ, Webb JA, Fuller RA, Richards RM. 2017. Simplifying the selection of evidence synthesis methods to inform environmental decisions: A guide for decision makers and scientists . Biological Conservation 213 : 135–145. [ Google Scholar ]
de Vrieze J. 2018. The metawars . Science 361 : 1184–1188. [ PubMed ] [ Google Scholar ]
Dicks LV, et al. 2017. . Knowledge Synthesis for Environmental Decisions: An Evaluation of Existing Methods, and Guidance for Their Selection, Use, and Development . EKLIPSE Project . [ Google Scholar ]
Diefenderfer HL, Johnson GE, Thom RM, Bunenau KE, Weitkamp LA, Woodley CM, Borde AB, Kropp RK. 2016. Evidence-based evaluation of the cumulative effects of ecosystem restoration . Ecosphere 7 : e01242. [ Google Scholar ]
Dietl GP. 2015. Evaluating the strength of escalation as a research program . Geological Society of America Abstracts with Programs 47 : 427. [ Google Scholar ]
Elton C, Nicholson M. 1942. The ten-year cycle in numbers of lynx in Canada . Journal of Animal Ecology 11 : 215–244. [ Google Scholar ]
Enders M, Hütt M-T, Jeschke JM. 2018. Drawing a map of invasion biology based on a network of hypotheses . Ecosphere 9 : e02146. [ Google Scholar ]
Enders M, et al. 2020. . A conceptual map of invasion biology: Integrating hypotheses into a consensus network . Global Ecology and Biogeography 29 : 978–999. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Farji-Brener AG, Amador-Vargas S. 2018. Hierarchy of hypotheses or hierarchy of predictions? Clarifying key concepts in ecological research . Pages 19–22 in Jeschke JM, Heger T, eds. Invasion Biology: Hypotheses and Evidence . CAB International. [ Google Scholar ]
Giere RN, Bickle J, Mauldin R. 2005. Understanding Scientific Reasoning , 5th ed. Wadsworth Cengage Learning. [ Google Scholar ]
Grace J, Anderson T, Olff H, Scheiner S. 2010. On the specification of structural equation models for ecological systems . Ecological Monographs 80 : 67–87. [ Google Scholar ]
Griesemer JR. 2013. Formalization and the meaning of “theory” in the inexact biological sciences . Biological Theory 7 : 298–310. [ Google Scholar ]
Griesemer J. 2018. Mapping theoretical and evidential landscapes in ecological science: Levin's virtue trade-off and the hierarchy-of-hypotheses approach . Pages 23–29 in Jeschke JM, Heger T, eds. Invasion Biology: Hypotheses and Evidence . CAB International. [ Google Scholar ]
Gurevitch J, Fox GA, Wardle GM, Inderjit Taub D. 2011. Emergent insights from the synthesis of conceptual frameworks for biological invasions . Ecology Letters 14 : 407–418. [ PubMed ] [ Google Scholar ]
Haddaway NR, Macura B, Whaley P, Pullin AS. 2018. ROSES: Reporting standards for systematic evidence syntheses: Pro forma, flow-diagram and descriptive summary of the plan and conduct of environmental systematic reviews and systematic maps . Environmental Evidence 7 : 7. [ Google Scholar ]
Heger T, Jeschke JM. 2014. The enemy release hypothesis as a hierarchy of hypotheses . Oikos 123 : 741–750. [ Google Scholar ]
Heger T, Jeschke JM. 2018a. Conclusions and outlook . Pages 167–172 in Jeschke JM, Heger T, eds. Invasion Biology: Hypotheses and Evidence . CAB International. [ Google Scholar ]
Heger T, Jeschke JM. 2018b. Enemy release hypothesis . Pages 92–102 in Jeschke JM, Heger T, eds. Invasion Biology: Hypotheses and Evidence . CAB International. [ Google Scholar ]
Heger T, Jeschke JM. 2018c. The hierarchy-of-hypotheses approach updated: A toolbox for structuring and analysing theory, research, and evidence . Pages 38–48 in Jeschke JM, Heger T, eds. Invasion Biology: Hypotheses and Evidence . CAB International. [ Google Scholar ]
Heger T, et al. 2013. Conceptual frameworks and methods for advancing invasion ecology . Ambio 42 : 527–540. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Heger T, et al. 2019. Towards an integrative, eco-evolutionary understanding of ecological novelty: Studying and communicating interlinked effects of global change . BioScience 69 : 888–899. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Howick J. 2011. The Philosophy of Evidence-based Medicine . Wiley-Blackwell. [ Google Scholar ]
Jeltsch F, et al. 2013. . How can we bring together empiricists and modelers in functional biodiversity research? Basic and Applied Ecology 14 : 93–101. [ Google Scholar ]
Jeschke JM, Heger T, eds. 2018a. Invasion Biology: Hypotheses and Evidence . CAB International. [ Google Scholar ]
Jeschke JM, Heger T, eds. 2018b. Synthesis . Pages 157–166 in Jeschke JM, Heger T, eds. Invasion Biology. Hypotheses and Evidence . CAB International. [ Google Scholar ]
Jeschke JM, Pyšek P. 2018. Tens rule . Pages 124–132 in Jeschke JM, Heger T, eds. Invasion Biology: Hypotheses and Evidence . CAB International. [ Google Scholar ]
Jeschke JM, Gómez Aparicio L, Haider S, Heger T, Lortie CJ, Pyšek P, Strayer DL. 2012. Support for major hypotheses in invasion biology is uneven and declining . NeoBiota 14 : 1–20. [ Google Scholar ]
Jeschke JM, Debille S, Lortie CJ. 2018a. Biotic resistance and island susceptibility hypotheses . Pages 60–70 in Jeschke JM, Heger T, eds. Invasion Biology: Hypotheses and Evidence . CAB International. [ Google Scholar ]
Jeschke JM, Enders M, Bagni M, Jeschke P, Zimmermann M, Heger T. 2018b. Hi Knowledge. Hi-Knowledge.org. www.hi-knowledge.org/invasion-biology [ Google Scholar ]
Jeschke JM, Lokatis S, Bartram I, Tockner K. 2019. Knowledge in the dark: Scientific challenges and ways forward . FACETS 4 : 1–19. [ Google Scholar ]
Keane RM, Crawley MJ. 2002. Exotic plant invasions and the enemy release hypothesis . Trends in Ecology and Evolution 17 : 164–170. [ Google Scholar ]
Koricheva J, Gurevitch J, Mengersen K, eds. 2013. Handbook of Meta-analysis in Ecology and Evolution . Princeton University Press. [ Google Scholar ]
Krebs CJ, Boonstra R, Boutin S. 2018. Using experimentation to understand the 10-year snowshoe hare cycle in the boreal forest of North America . Journal of Animal Ecology 87 : 87–100. [ PubMed ] [ Google Scholar ]
Krebs CJ, Boonstra R, Boutin S, Sinclair ARE. 2001. What drives the 10-year cycle of snowshow hares? BioScience 51 : 25–35. [ Google Scholar ]
Lortie CJ. 2014. Formalized synthesis opportunities for ecology: Systematic reviews and meta-analyses . Oikos 123 : 897–902. [ Google Scholar ]
MacLulich DA. 1937. Fluctuation in numbers of the varying hare (Lepus americanus) . Univ Toronto Studies Biol Series 43 : 1–136. [ Google Scholar ]
Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, Shekelle P, Stewart LA, PRISMA-P Group . 2015. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement . Systematic Reviews 4 : 1. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Mupepele A-C, Walsh JC, Sutherland WJ, Dormann CF. 2016. An evidence assessment tool for ecosystem services and conservation studies . Ecological Applications 26 : 1295–1301. [ PubMed ] [ Google Scholar ]
Nakagawa S, Samarasinghe G, Haddaway NR, Westgate MJ, O'Dea RE, Noble DWA, Lagisz M. 2019. Research weaving: Visualizing the future of research synthesis . Trends in Ecology and Evolution 34 : 224–238. [ PubMed ] [ Google Scholar ]
Nesshöver C, et al. 2016. . The Network of Knowledge approach: Improving the science and society dialogue on biodiversity and ecosystem services in Europe . Biodiversity and Conservation 25 : 1215–1233. [ Google Scholar ]
Norris RH, Webb JA, Nichols SJ, Stewardson MJ, Harrison ET. 2012. Analyzing cause and effect in environmental assessments: Using weighted evidence from the literature . Freshwater Science 31 : 5–21. [ Google Scholar ]
Oli MK, Krebs CJ, Kenney AJ, Boonstra R, Boutin S, Hines JE. 2020. Demography of snowshoe hare population cycles . Ecology 101 : e02969. doi:10.1002/ecy.2969. [ PubMed ] [ Google Scholar ]
Pickett STA, Kolasa J, Jones CG. 2007. Ecological Understanding: The Nature of Theory and the Theory of Nature , 2nd ed. Academic Press. [ Google Scholar ]
Platt JR. 1964. Strong inference . Science 146 : 347–353. [ PubMed ] [ Google Scholar ]
Pullin A, et al. 2016. . Selecting appropriate methods of knowledge synthesis to inform biodiversity policy . Biodiversity and Conservation 25 : 1285–1300. [ Google Scholar ]
Ryo M, Jeschke JM, Rillig MC, Heger T. 2019. Machine learning with the hierarchy-of-hypotheses (HoH) approach discovers novel pattern in studies on biological invasions . Research Synthesis Methods 11 : 66–73doi:10.1002/jrsm.1363. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Scheiner SM. 2013. The ecological literature, an idea-free distribution . Ecology Letters 16 : 1421–1423. [ PubMed ] [ Google Scholar ]
Scheiner SM, Fox GA. 2018. A hierarchy of hypotheses or a network of models . Pages 30–37 in Jeschke JM, Heger T, eds. Invasion Biology: Hypotheses and Evidence . CAB International. [ Google Scholar ]
Schulz AN, Lucardi RD, Marsico TD. 2019. Successful invasions and failed biocontrol: The role of antagonistic species interactions . BioScience 69 : 711–724. [ Google Scholar ]
Silvertown JW, Charlesworth D. 2001. Introduction to Plant Population Biology . Blackwell Scientific. [ Google Scholar ]
Stenseth NC, Falck W, Bjørnstad ON, Krebs CJ. 1997. Population regulation in snowshoe hare and Canadian lynx: Asymmetric food web configurations between hare and lynx . Proceedings of the National Academy of Sciences of the United States of America 94 : 5147–5152. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Sutherland WJ. 2006. Predicting the ecological consequences of environmental change: A review of the methods . Journal of Applied Ecology 43 : 599–616. [ Google Scholar ]
Sutherland WJ, et al. 2013. . Identification of 100 fundamental ecological questions . Journal of Ecology 101 : 58–67. [ Google Scholar ]
Thompson JN. 2005. The Geographic Mosaic of Coevolution . University of Chicago Press. [ Google Scholar ]
Van Valen L. 1973. A new evolutionary law . Evolutionary Theory 1 : 1–30. [ Google Scholar ]
Vermeij GJ. 1987. Evolution and Escalation: An Ecological History of Life . Princeton University Press. [ Google Scholar ]
Wu L, Huang I-C, Huang W-C, Du P-L. 2019. Aligning organizational culture and operations strategy to improve innovation outcomes: An integrated perspective in organizational management . Journal of Organizational Change Management 32 : 224–250. doi:10.1108/JOCM-03-2018-0073. [ Google Scholar ]

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HYPOTHESIS Synonyms: 35 Similar and Opposite Words
Synonyms for HYPOTHESIS: theory, thesis, proposition, premise, assumption, suggestion, guess, supposition; Antonyms of HYPOTHESIS: fact, knowledge, assurance, certainty
Hypothesis
Biology definition: A hypothesis is a supposition or tentative explanation for (a group of) phenomena, (a set of) facts, or a scientific inquiry that may be tested, verified or answered by further investigation or methodological experiment. It is like a scientific guess. It's an idea or prediction that scientists make before they do ...
The scientific method (article)
The scientific method. At the core of biology and other sciences lies a problem-solving approach called the scientific method. The scientific method has five basic steps, plus one feedback step: Make an observation. Ask a question. Form a hypothesis, or testable explanation. Make a prediction based on the hypothesis.
4.14: Experiments and Hypotheses
Experiments and further observations are often used to test the hypotheses. A scientific experiment is a carefully organized procedure in which the scientist intervenes in a system to change something, then observes the result of the change. Scientific inquiry often involves doing experiments, though not always.
52 Synonyms & Antonyms for HYPOTHESIS
Find 52 different ways to say HYPOTHESIS, along with antonyms, related words, and example sentences at Thesaurus.com.
Hypothesis
hypothesis: 1 n a tentative insight into the natural world; a concept that is not yet verified but that if true would explain certain facts or phenomena "a scientific hypothesis that survives experimental testing becomes a scientific theory" Synonyms: possibility , theory Types: show 17 types... hide 17 types... hypothetical a hypothetical ...
Hypothesis Definition & Meaning
hypothesis: [noun] an assumption or concession made for the sake of argument. an interpretation of a practical situation or condition taken as the ground for action.
What is a scientific hypothesis?
Bibliography. A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method. Many describe it as an ...
How to Write a Strong Hypothesis
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. If a first-year student starts attending more lectures, then their exam scores will improve.
hypothesis noun
1 [countable] 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 synonym theory to formulate/confirm a hypothesis a hypothesis about the function of dreams There is little evidence to support these hypotheses. Topic Collocations Scientific Research theory. formulate/advance a theory/hypothesis
Experiments and Hypotheses
When conducting scientific experiments, researchers develop hypotheses to guide experimental design. A hypothesis is a suggested explanation that is both testable and falsifiable. You must be able to test your hypothesis through observations and research, and it must be possible to prove your hypothesis false. For example, Michael observes that ...
What Is a Hypothesis? The Scientific Method
A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject. In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.
Biology Hypothesis
A hypothesis in biology is a critical component of scientific research that proposes an explanation for a specific biological phenomenon. Writing a well-formulated hypothesis sets the foundation for conducting experiments, making observations, and drawing meaningful conclusions. Follow this step-by-step guide to create a strong biology hypothesis:
Hypothesis Definition & Meaning
Hypothesis definition, a proposition, or set of propositions, set forth as an explanation for the occurrence of some specified group of phenomena, either asserted merely as a provisional conjecture to guide investigation (working hypothesis ) or accepted as highly probable in the light of established facts. See more.
How to Write a Hypothesis in 6 Steps, With Examples
4 Alternative hypothesis. An alternative hypothesis, abbreviated as H 1 or H A, is used in conjunction with a null hypothesis. It states the opposite of the null hypothesis, so that one and only one must be true. Examples: Plants grow better with bottled water than tap water. Professional psychics win the lottery more than other people. 5 ...
Hypothesis Examples
Here are some research hypothesis examples: If you leave the lights on, then it takes longer for people to fall asleep. If you refrigerate apples, they last longer before going bad. If you keep the curtains closed, then you need less electricity to heat or cool the house (the electric bill is lower). If you leave a bucket of water uncovered ...
HYPOTHESIS
HYPOTHESIS meaning: 1. an idea or explanation for something that is based on known facts but has not yet been proved…. Learn more.
On the role of hypotheses in science
Scientific research progresses by the dialectic dialogue between hypothesis building and the experimental testing of these hypotheses. Microbiologists as biologists in general can rely on an increasing set of sophisticated experimental methods for hypothesis testing such that many scientists maintain that progress in biology essentially comes with new experimental tools.
HYPOTHESIS definition
HYPOTHESIS meaning: a suggested explanation for something that has not yet been proved to be true. Learn more.
HYPOTHESIS
HYPOTHESIS - Synonyms, related words and examples | Cambridge English Thesaurus
Hypothesis
Hypothesis means something taken or supposed for granted, with the object of following out its consequences. In Greek, the term hypothesis is "a putting under," and in Latin, it is equivalent to being suppositio. Scientific Hypothesis. In the plan of an action course, one may consider different alternatives, working out each in a detailed way.
The Hierarchy-of-Hypotheses Approach: A Synthesis Method for Enhancing
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 ...
hypothesis
hypothesis - WordReference English dictionary, questions, discussion and forums. All Free. ... Philosophy, Biology, Physics a theory or idea that is put forth to explain something, and that is either accepted as a guide for future investigation or is assumed for the sake of argument and testing.
Estimating the selection pressure and evolutionary rate of ...
bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution ... Estimating the selection pressure and evolutionary rate of proteins on the non-neutral hypothesis of synonymous mutations. View ORCID Profile Jiachen Ye, View ORCID Profile Chunmei Cui, View ORCID Profile Rui Fan ...