formulate your hypothesis list down 3 hypothesis in research

How to Write a Hypothesis in 6 Steps, With Examples

A hypothesis is a statement that explains the predictions and reasoning of your research—an “educated guess” about how your scientific experiments will end. As a fundamental part of the scientific method, a good hypothesis is carefully written, but even the simplest ones can be difficult to put into words.

Want to know how to write a hypothesis for your academic paper ? Below we explain the different types of hypotheses, what a good hypothesis requires, the steps to write your own, and plenty of examples.

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What is a hypothesis?

One of our 10 essential words for university success , a hypothesis is one of the earliest stages of the scientific method. It’s essentially an educated guess—based on observations—of what the results of your experiment or research will be.

Some hypothesis examples include:

If I water plants daily they will grow faster.
Adults can more accurately guess the temperature than children can.
Butterflies prefer white flowers to orange ones.

If you’ve noticed that watering your plants every day makes them grow faster, your hypothesis might be “plants grow better with regular watering.” From there, you can begin experiments to test your hypothesis; in this example, you might set aside two plants, water one but not the other, and then record the results to see the differences.

The language of hypotheses always discusses variables , or the elements that you’re testing. Variables can be objects, events, concepts, etc.—whatever is observable.

There are two types of variables: independent and dependent. Independent variables are the ones that you change for your experiment, whereas dependent variables are the ones that you can only observe. In the above example, our independent variable is how often we water the plants and the dependent variable is how well they grow.

Hypotheses determine the direction and organization of your subsequent research methods, and that makes them a big part of writing a research paper . Ultimately the reader wants to know whether your hypothesis was proven true or false, so it must be written clearly in the introduction and/or abstract of your paper.

7 examples of hypotheses

Depending on the nature of your research and what you expect to find, your hypothesis will fall into one or more of the seven main categories. Keep in mind that these categories are not exclusive, so the same hypothesis might qualify as several different types.

1 Simple hypothesis

A simple hypothesis suggests only the relationship between two variables: one independent and one dependent.

If you stay up late, then you feel tired the next day.
Turning off your phone makes it charge faster.

2 Complex hypothesis

A complex hypothesis suggests the relationship between more than two variables, for example, two independents and one dependent, or vice versa.

People who both (1) eat a lot of fatty foods and (2) have a family history of health problems are more likely to develop heart diseases.
Older people who live in rural areas are happier than younger people who live in rural areas.

3 Null hypothesis

A null hypothesis, abbreviated as H 0 , suggests that there is no relationship between variables.

There is no difference in plant growth when using either bottled water or tap water.
Professional psychics do not win the lottery more than other people.

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.

Plants grow better with bottled water than tap water.
Professional psychics win the lottery more than other people.

5 Logical hypothesis

A logical hypothesis suggests a relationship between variables without actual evidence. Claims are instead based on reasoning or deduction, but lack actual data.

An alien raised on Venus would have trouble breathing in Earth’s atmosphere.
Dinosaurs with sharp, pointed teeth were probably carnivores.

6 Empirical hypothesis

An empirical hypothesis, also known as a “working hypothesis,” is one that is currently being tested. Unlike logical hypotheses, empirical hypotheses rely on concrete data.

Customers at restaurants will tip the same even if the wait staff’s base salary is raised.
Washing your hands every hour can reduce the frequency of illness.

7 Statistical hypothesis

A statistical hypothesis is when you test only a sample of a population and then apply statistical evidence to the results to draw a conclusion about the entire population. Instead of testing everything , you test only a portion and generalize the rest based on preexisting data.

In humans, the birth-gender ratio of males to females is 1.05 to 1.00.
Approximately 2% of the world population has natural red hair.

What makes a good hypothesis?

No matter what you’re testing, a good hypothesis is written according to the same guidelines. In particular, keep these five characteristics in mind:

Cause and effect

Hypotheses always include a cause-and-effect relationship where one variable causes another to change (or not change if you’re using a null hypothesis). This can best be reflected as an if-then statement: If one variable occurs, then another variable changes.

Testable prediction

Most hypotheses are designed to be tested (with the exception of logical hypotheses). Before committing to a hypothesis, make sure you’re actually able to conduct experiments on it. Choose a testable hypothesis with an independent variable that you have absolute control over.

Independent and dependent variables

Define your variables in your hypothesis so your readers understand the big picture. You don’t have to specifically say which ones are independent and dependent variables, but you definitely want to mention them all.

Candid language

Writing can easily get convoluted, so make sure your hypothesis remains as simple and clear as possible. Readers use your hypothesis as a contextual pillar to unify your entire paper, so there should be no confusion or ambiguity. If you’re unsure about your phrasing, try reading your hypothesis to a friend to see if they understand.

Adherence to ethics

It’s not always about what you can test, but what you should test. Avoid hypotheses that require questionable or taboo experiments to keep ethics (and therefore, credibility) intact.

How to write a hypothesis in 6 steps

1 ask a question.

Curiosity has inspired some of history’s greatest scientific achievements, so a good place to start is to ask yourself questions about the world around you. Why are things the way they are? What causes the factors you see around you? If you can, choose a research topic that you’re interested in so your curiosity comes naturally.

2 Conduct preliminary research

Next, collect some background information on your topic. How much background information you need depends on what you’re attempting. It could require reading several books, or it could be as simple as performing a web search for a quick answer. You don’t necessarily have to prove or disprove your hypothesis at this stage; rather, collect only what you need to prove or disprove it yourself.

3 Define your variables

Once you have an idea of what your hypothesis will be, select which variables are independent and which are dependent. Remember that independent variables can only be factors that you have absolute control over, so consider the limits of your experiment before finalizing your hypothesis.

4 Phrase it as an if-then statement

When writing a hypothesis, it helps to phrase it using an if-then format, such as, “ If I water a plant every day, then it will grow better.” This format can get tricky when dealing with multiple variables, but in general, it’s a reliable method for expressing the cause-and-effect relationship you’re testing.

5 Collect data to support your hypothesis

A hypothesis is merely a means to an end. The priority of any scientific research is the conclusion. Once you have your hypothesis laid out and your variables chosen, you can then begin your experiments. Ideally, you’ll collect data to support your hypothesis, but don’t worry if your research ends up proving it wrong—that’s all part of the scientific method.

6 Write with confidence

Last, you’ll want to record your findings in a research paper for others to see. This requires a bit of writing know-how, quite a different skill set than conducting experiments.

That’s where Grammarly can be a major help; our writing suggestions point out not only grammar and spelling mistakes , but also new word choices and better phrasing. While you write, Grammarly automatically recommends optimal language and highlights areas where readers might get confused, ensuring that your hypothesis—and your final paper—are clear and polished.

formulate your hypothesis list down 3 hypothesis in research

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Writing Guides / How to Write a Hypothesis w/ Strong Examples

How to Write a Hypothesis w/ Strong Examples

A hypothesis is a guess about what’s going to happen. In research, the hypothesis is what you the researcher expects the outcome of an experiment, a study, a test, or a program to be. It is a belief based on the evidence you have before you, the reasoning of your mind, and what prior experience tells you. The hypothesis is not 100% guaranteed—that’s why there are different kinds of hypotheses. In this article, we’ll explain what those are when they should be used. So let’s dive in!

What is a Hypothesis / Definition

A hypothesis is like a bet: you size things up and tell your mates exactly what you think is going to happen with respect to X, Y, Z. It can also be like an explanation for a phenomenon, or a logical prediction of a possible causal correlation among multiple factors. In science—or, really, in any field, a hypothesis is used as a basis for further investigation. For example, many qualitative or exploratory studies are conducted just so that the researcher in the end can formulate a hypothesis after all the data is collected an analyzed.

In short, it is an educated guess, based on existing knowledge or observation. It is a way of proposing a possible explanation for a relationship between variables.

One thing to remember is this: the key characteristic of a hypothesis is that it must be testable and potentially falsifiable. This means that it should be possible to design an experiment or observation that could potentially prove the hypothesis wrong. That is a very important point to keep in mind.

For that reason, hypotheses are usually only formulated after conducting a preliminary review of existing literature, observations, or after obtaining a general understanding of the subject area. They are not random guesses. They are grounded in some form of evidence or understanding of the phenomena being studied. The formulation of a hypothesis is a big step in the scientific method, as it defines the focus and direction of the research. A lot of time is often spent simply on developing a good hypothesis.

Why? A well-constructed hypothesis not only proposes an explanation for an observation but also often predicts measurable and testable outcomes. It is not merely a question, but rather a statement that includes a clear explanation or prediction. For example, rather than asking “Does temperature affect the growth of bacteria?”, a hypothesis would be something like this: “If the temperature increases, then the growth rate of bacteria will increase.” It is clear, measurable, testable, and potentially falsifiable.

In the scientific community, a hypothesis is respected when it has the potential to advance knowledge, regardless of whether testing proves it to be true or false. The process of testing, refining, or nullifying hypotheses through experimentation and observation is part of what research is all about.

Different types of Hypotheses

Hypotheses can be categorized into several types. Each type has a unique purpose in scientific research. Understanding these types is helpful for formulating a hypothesis that is appropriate to your specific research question. The main types of hypotheses include the following:

Simple Hypothesis : This formulates a relationship between two variables, one independent and one dependent. It is straightforward and concise, making it easy to test. It is most often used in basic scientific experiments where the aim is to investigate the relationship between two variables, such as in laboratory experiments or controlled field studies.
Complex Hypothesis : Unlike the simple hypothesis, a complex hypothesis involves multiple independent and dependent variables. It is used in studies that are looking at several factors simultaneously, where there is an interplay of multiple variables. These are common in fields like social sciences, behavioral studies, and large-scale environmental research.
Directional Hypothesis : This type predicts the nature of the effect of the independent variable on the dependent variable. It specifies the direction of the expected relationship. It tends to be used studies where prior research or theory has already suggested a specific direction of influence or effect, such as in clinical trials or in studies testing theoretical models.
Non-directional Hypothesis : In contrast to the directional hypothesis, a non-directional hypothesis does not specify the direction of the relationship. It simply suggests that there is a relationship between variables without stating whether it is positive or negative. It is often used in exploratory research where the direction of the relationship is not known, such as in early-stage psychological research or when studying new phenomena.
Null Hypothesis : The null hypothesis states that there is no relationship between the variables being studied. It is a default position that assumes no effect until evidence suggests otherwise. It is also a fundamental aspect of virtually all quantitative research, serving as the hypothesis that there is no effect or no difference, against which the alternative hypothesis is tested.
Associative and Causal Hypotheses : Associative hypotheses propose a relationship between variables where changes in one variable correspond with changes in another. They are common in observational studies, such as epidemiological research or surveys, where the goal is to identify correlations between variables. Causal hypotheses go a step further by suggesting that one variable causes the change in the other. They are used in experimental research designed to determine cause-and-effect relationships, such as randomized controlled trials in medical research or controlled experiments in psychology.

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How to Write a Good Hypothesis

Writing a good hypothesis is definitely a good skill to have in scientific research. But it is also one that you can definitely learn with some practice if you don’t already have it. Just keep in mind that the hypothesis is what sets the stage for the entire investigation. It guides the methods and analysis. Everything you do in research stems from your research question and hypothesis.

Here are four essential steps to follow when crafting a hypothesis:

Start with a Research Question

Every hypothesis begins with a clear, focused research question. This question should arise from a review of existing literature, some observations you have made in the field, or an information gap that is apparent in current knowledge. The question should be specific and researchable. For example, instead of a broad question like “What affects plant growth?”, a more specific question would be “How does the amount of water affect the growth of sunflowers?” This is a specific question, and sets up a stage for a perfect hypothesis.

How did you develop the question? Easy. You simply took a broad view first, and then began looking more closely. You looked into the subject matter. And, as with anything, the more you look into it, the more likely you are to have questions. So, the most important step here is to get a sense of your subject. The more you learn about it, the more likely you will be to have a good research question. Ask yourself: what about this subject would I like to know more about? It helps if you have a genuine interest in the topic! Say, for example, you want to know more about cryptocurrency security or scalability: wouldn’t you start asking questions about how to achieve either? And wouldn’t you need to know a bit about the topic before you can ask the right question? Of course! Apply that same logic to whatever subject you are researching and your research question will appear rather quickly.

Do Preliminary Research

Before formulating your hypothesis, you of course should conduct preliminary research. This involves reviewing existing literature, understanding the current state of knowledge in the field, doing some critical thinking on the subject, and considering any existing theories and findings that might be relevant. This preliminary research helps in developing an educated guess. If you do your background research well, your hypothesis will be grounded in existing knowledge.

This is basically the step that comes after you ask your research question but before you make a prediction about the subject matter. Just like if you went to a racetrack and wanted to place a bet on a horse, you would research the horses, the owners, the teams, and make an educated guess about which one is most likely to win, doing preliminary research is the same: you want to become very familiar with the topic—know it inside and out. Then you will have everything you need to formulate your hypothesis.

Formulate the Hypothesis

Based on your research question and preliminary research, now you can create your hypothesis. A good hypothesis should be clear, concise, and testable. It typically takes a statement form, predicting a potential outcome or relationship between variables. Make sure that your hypothesis is focused and answers your research question. For example, a hypothesis for the research question stated above might be: “If sunflower plants are watered with varying amounts of water, then those watered more frequently will grow taller due to better hydration.”

Keep in mind that when you reach the stage of formulating your hypothesis, you are essentially ready to make a statement that can be tested through research or experimentation. Your hypothesis should be as precise as possible. Don’t ever use ambiguous language in your hypothesis. Also, you should be very specific about the variables involved and the expected relationship between them (if applicable). For example, let’s look at the hypothesis we generated above: “If sunflower plants are watered with varying amounts of water, then those watered more frequently will grow taller due to better hydration.” We have clearly identified the variables (frequency of watering and plant growth height) and the expected outcome.

But what else should your hypothesis do? Well, when we say it should address your research question, we mean it should be a logical extension of the question and your preliminary research. If your research question is about the effect of watering frequency on sunflower growth, your hypothesis should specifically predict how these two variables are related. It should not get into the types of soil, sunshine, temperature, or other variables unless these were brought up specifically in your research question.

Above all, you want your hypothesis to make a prediction. This means stating an expected outcome based on your understanding of the subject. The prediction is what will be tested through experiments or observations.

Ensure Testability and Falsifiability

An important aspect of a good hypothesis is that it must be testable and potentially falsifiable. This means you should be able to conduct experiments or make observations that can support or refute the hypothesis. Avoid vague or broad statements that cannot be empirically tested. Also, make sure that your hypothesis is potentially falsifiable; i.e., there should exist the possibility that it can be proven wrong. For example, a hypothesis like “Sunflower plants need water to grow” is not falsifiable, as it is already a well-established fact. But a hypothesis regarding frequency or amount of watering does have the potential to be nullified.

Therefore, keep that in mind during this step: for a hypothesis to be testable, there must be a way to conduct an experiment or make observations that can confirm or disprove it. This means you should be able to measure or observe the variables involved. In the sunflower example, you can measure plant growth and control the frequency of watering very easily. This is precisely what makes the hypothesis testable.

Another important point is falsifiability, as this is what separates scientific hypotheses from non-scientific ones. If it doesn’t have the potential to be proven wrong, it’s not a hypothesis. Being falsifiable doesn’t mean a hypothesis is false. It means that if the hypothesis is false, there is a way to demonstrate this. The potential for falsification is what allows researchers to make scientific progress no matter the problem or field.

Also, don’t be vague. Your hypothesis needs to be specific: hypotheses that are too vague or broad are not useful in research, as there is no way to test them. For example, saying “Water affects plant growth” is too vague. How does water affect growth? Is it the amount, frequency, or type of water? Such a hypothesis needs to be more specific to be testable. See what we mean?

Remember: A hypothesis does not need to be correct. It just needs to be testable. It is a starting point for investigation. The value of a hypothesis lies in its ability to be tested. The results of that test are what can potentially contribute to the existing body of scientific knowledge, regardless of whether the hypothesis is supported or refuted by the resulting data.

Hypothesis Examples

Simple hypothesis examples.

Increasing the amount of natural light in a classroom will improve students’ test scores.
Drinking at least eight glasses of water a day reduces the frequency of headaches in adults.
Plant growth is faster when the plant is exposed to music for at least one hour per day.

Complex Hypothesis Examples

Students’ academic performance is influenced by their study habits, family income, and the educational level of their parents.
Employee productivity is affected by workplace environment, job satisfaction, and the level of personal stress the worker encounters both on the job and at home.
The effectiveness of a weight loss program is dependent on the participant’s age, gender, and adherence to an appropriate diet plan.

Directional Hypothesis Examples

Exposure to high levels of air pollution during pregnancy will increase the risk of asthma in children.
A diet high in antioxidants will decrease the risk of heart disease in middle-aged adults.
Regular physical exercise leads to a significant decrease in the symptoms of depression in adults.

Non-directional Hypothesis Examples

There is a relationship between the amount of sleep a person gets and their level of stress.
A change in classroom environment has an effect on student concentration.
The introduction of ergonomics in the workplace environment impacts employee productivity.

Null Hypothesis Examples

There is no significant difference in test scores between students who study in groups and those who study alone.
Dietary changes have no effect on the improvement of symptoms in patients with type 2 diabetes.
The new marketing strategy does not affect the sales numbers of the product.

Associative Hypothesis Examples

There is an association between the number of hours spent on social media and the level of anxiety in teenagers.
Daily consumption of green tea is associated with weight loss in adults.
The frequency of public transport use correlates with the level of urban air pollution.

Causal Hypotheses Examples

Implementing a school-based exercise program causes a reduction in obesity rates among children.
High levels of job stress cause an increase in blood pressure.
Smoking causes an increase in the risk of developing lung cancer.

In conclusion, understanding and effectively formulating a solid hypothesis is what scientific research and inquiry is all about—regardless of the type of work you’re doing. It may be a simple, complex, directional, non-directional, null, associative, or causal hypothesis—no matter: each type has its own specific purpose and guides the direction of a study in a different way. A simple hypothesis explores the relationship between two variables, while a complex hypothesis involves multiple variables. Directional hypotheses specify the expected direction of a relationship, whereas non-directional hypotheses do not. The null hypothesis, a fundamental aspect of statistical testing, posits no effect or relationship, serving as a baseline for analysis. Associative hypotheses explore correlations between variables, and causal hypotheses aim to establish cause-and-effect relationships.

The ability to craft a clear, concise, and testable hypothesis is important for any researcher. It is what shapes the course of the investigation. It is also the backbone of the scientific method itself. A well-formulated hypothesis can lead to groundbreaking research or make significant contributions to knowledge in different fields.

As we have shown you with our examples, the hypothesis is more than a mere guess; it is an educated, testable prediction that guides you through the process of scientific discovery. When you master the art of hypothesis formulation, you can set off on your investigation with a clear roadmap and a clear sense of purpose.