How to Write a Hypothesis
Two Parts:Preparing to Write a HypothesisFormulating Your HypothesisCommunity Q&A
A hypothesis is a description of a pattern in nature or an explanation about some real-world phenomenon that can be tested through observation and experimentation. The most common way a hypothesis is used in scientific research is as a tentative, testable, and falsifiable statement that explains some observed phenomenon in nature.ok ok We more specifically call this kind of statement an explanatory hypothesis. However, a hypothesis can also be a statement that describes an observed pattern in nature. In this case we call the statement a generalizing hypothesis. Hypotheses can generate predictions: statements that propose that one variable will drive some effect on or change in another variable in the result of a controlled experiment. However, many science resources promote the myth that a hypothesis is simply an educated guess and no different from a prediction. More on this misunderstanding below.
Many academic fields, from the physical sciences to the life sciences to the social sciences, use hypothesis testing as a means of testing ideas to learn about the world and advance scientific knowledge. Whether you are a beginning scholar or a beginning student taking a class in a science subject, understanding what hypotheses are and being able to generate hypotheses and predictions yourself is very important. These instructions will help get you started.
Part 1Preparing to Write a Hypothesis
1Select a topic. Pick a topic that interests you, and that you think it would be good to know more about.
- If you are writing a hypothesis for a school assignment, this step may be taken care of for you.
2Read existing research. Gather all the information you can about the topic you've selected. You'll need to become an expert on the subject and develop a good grasp of what is already known about the topic.
- Focus on academic and scholarly writing. You need to be certain that your information is unbiased, accurate, and comprehensive.
- You can find information in textbooks, at a library, and online. If you are in school, you can also ask for help from teachers, librarians, and your peers.
3Analyze the literature. Spend some time reading the materials you've collected. As you do so, look for and make note of unanswered questions in the literature. These can provide excellent ideas for areas to investigate.
- For example, if you are interested in the effects of caffeine on the human body, but notice that nobody seems to have explored whether caffeine affects males differently than it does females, this could be something to formulate a hypothesis about. Or, if you are interested in organic farming, you might notice that no one has tested whether organic fertilizer results in different growth rates for plants than non-organic fertilizer.
- You can sometimes find holes in the existing literature by looking for statements like “it is unknown” or places where information is clearly missing. You might also find a claim in the literature that seems far-fetched, unlikely, or too good to be true, like that caffeine improves math skills. If the claim is testable, you could provide a great service to scientific knowledge by doing your own investigation. If you confirm the claim, the claim becomes even more credible. If you do not find support for the claim, you are helping with the necessary self-correcting aspect of science.
- Examining these types of questions provides an excellent way for you to set yourself apart by filling in important gaps in a field of study.
4Generate questions. After studying the literature on your topic, generate one or more unanswered questions you'd be interested in exploring further. These are your research questions.
- Following the examples above, you might ask: "How does caffeine affect females as compared to males?" or "How does organic fertilizer affect plant growth compared to non-organic fertilizer?" The rest of your research will be aimed at answering these questions.
5Look for clues as to what the answer might be. Once you have generated your research question or questions, look in the literature to see if the existing findings and/or theories about the topic provide any clues that would allow you to come up with ideas about what the answers to your research questions might be. If so, these clues can form the basis for your hypothesis.
- Following the examples above, if you discover in the literature that there is a pattern that some other types of stimulants seem to affect females more than males, this could be a clue that the same pattern might be true for caffeine. Similarly, if you observe the pattern that organic fertilizer seems to be associated with smaller plants overall, you might explain this pattern with the hypothesis that plants exposed to organic fertilizer grow more slowly than plants exposed to non-organic fertilizer.
Part 2Formulating Your Hypothesis
1Determine your variables. A generalizing hypothesis describes a pattern you think may exist between two variables: an independent variable and a dependent variable. If your experiments confirm the pattern, you may decide to suggest a reason that the pattern exists or a mechanism that generates the pattern. The reason or mechanism you suggest is an explanatory hypothesis.
- You can think of the independent variable as the one that is causing some kind of difference or effect to occur. In the examples, the independent variable would be biological sex, i.e. whether a person is male or female, and fertilizer type, i.e. whether the fertilizer is organic or non-organically-based.
- The dependent variable is what is affected by (i.e. "depends" on) the independent variable. In the examples above, the dependent variable would be the measured impact of caffeine or fertilizer.
- Your hypothesis should only suggest one relationship. Most importantly, it should only have one independent variable. If you have more than one, you won't be able to determine which one is actually the source of any effects you might observe.
2Generate a simple hypothesis. Once you've spent some time thinking about your research question and variables, write down your initial idea about how the variables might be related as a simple declarative statement.
- Don't worry too much at this point about being precise or detailed.
- In the examples above, one hypothesis would make a statement about whether a person's biological sex might impact the way the person is affected by caffeine; for example, at this point, your hypothesis might simply be: "a person's biological sex is related to how caffeine affects his or her heart rate." The other hypothesis would make a general statement about plant growth and fertilizer; for example your simple explanatory hypothesis might be "plants given different types of fertilizer are different sizes because they grow at different rates."
3Decide on direction. Hypotheses can either be directional or non-directional. A non-directional hypothesis simply says that one variable affects the other in some way, but does not say specifically in what way. A directional hypothesis provides more information about the nature (or "direction") of the relationship, stating specifically how one variable affects the other.
- Using our example, our non-directional hypotheses would be "there is a relationship between a person's biological sex and how much caffeine increases the person's heart rate," and "there is a relationship between fertilizer type and the speed at which plants grow."
- Directional predictions using the same example hypotheses above would be : "Females will experience a greater increase in heart rate after consuming caffeine than will males," and "plants fertilized with non-organic fertilizer will grow faster than those fertilized with organic fertilizer." Indeed, these predictions and the hypotheses that allow for them are very different kinds of statements. More on this distinction below.
- If the literature provides any basis for making a directional prediction, it is better to do so, because it provides more information. Especially in the physical sciences, non-directional predictions are often seen as inadequate.
4Get specific. Once you have an initial idea on paper, it's time to start refining. Make your hypotheses as specific as you can, so it's clear exactly what ideas you will be testing and make your predictions specific and measurable so that they provide evidence of a relationship between the variables.
- Where necessary, specify the population (i.e. the people or things) about which you hope to uncover new knowledge. For example, if you were only interested the effects of caffeine on elderly people, your prediction might read: "Females over the age of 65 will experience a greater increase in heart rate than will males of the same age." If you were interested only in how fertilizer affects tomato plants, your prediction might read: "Tomato plants treated with non-organic fertilizer will grow faster in the first three months than will tomato plants treated with organic fertilizer."
5Make sure it is testable. Your hypothesis must suggest a relationship between two variables or a reason that two variables are related that can feasibly be observed and measured in the real and observable world.
- For example, you would not want to make the hypothesis: "red is the prettiest color." This statement is an opinion and it cannot be tested with an experiment. However, proposing the generalizing hypothesis that red is the most popular color is testable with a simple random survey. If you do indeed confirm that red is the most popular color, your next step may be to ask: Why is red the most popular color? The answer you propose is your explanatory hypothesis.
- Often, hypotheses are stated in the form of if-then sentences. For example, "if children are given caffeine, then their heart rates will increase." This statement is not a hypothesis. This kind of statement is a brief description of an experimental method followed by a prediction and is the most common way that hypotheses are misrepresented in science education. An easy way to get to the hypothesis for this method and prediction is to ask yourself why you think heart rates will increase if children are given caffeine. Your explanatory hypothesis in this case may be that caffeine is a stimulant. At this point, some scientists write what is called a research hypothesis, a statement that includes the hypothesis, the experiment, and the prediction all in one statement: If caffeine is a stimulant, and some children are given a drink with caffeine while others are given a drink without caffeine, then the heart rates of those children given a caffeinated drink will increase more than the heart rate of children given a non-caffeinated drink.
- It may sound strange, but researchers rarely ever prove that a hypothesis is right or wrong. Instead, they look for evidence that the opposite of their hypotheses is probably not true. If the opposite (caffeine is not a stimulant) is probably not true, the hypothesis (caffeine is a stimulant) probably is true.
- Using the above example, if you were to test the effects of caffeine on the heart rates of children, evidence that your hypothesis is not true, sometimes called the null hypothesis, could occur if the heart rates of both the children given the caffeinated drink and the children given the non-caffeinated drink (called the placebo control) did not change, or lowered or raised with the same magnitude, if there was no difference between the two groups of children. If you wanted to test the effects of different fertilizer types, evidence that your hypothesis was not true would be that the plants grew at the same rate, regardless of fertilizer, or if plants treated with organic fertilizer grew faster. It is important to note here that the null hypothesis actually becomes much more useful when researchers test the significance of their results with statistics. When statistics are used on the results of an experiment, a researcher is testing the idea of the null statistical hypothesis. For example, that there is no relationship between two variables or that there is no difference between two groups.
Test your hypothesis. Make your observations or conduct your experiment. Your evidence may allow you to reject your null hypotheses, thus lending support to your experimental hypothesis. However, your evidence may not allow you to reject your null hypothesis and this is okay. Any result is important, even when your result sends you back to the drawing board. Constantly having to go "back to the drawing board" and refine your ideas is how authentic science really works!
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Yes, I read the article
How can I improve my hypothesis?
You have to identify the independent and the dependent variables of the experiment, add it to your hypothesis, and that's it, just make sure your hypothesis is specific!
Why is a hypothesis necessary?
A hypothesis is necessary as it explains what you predict will happen. When you find the results, you can see what actually happened and whether or not your prediction was correct or similar to the results.
What is a hypothesis?
A hypothesis is a supposition gathered by reasoning after consideration of the available evidence; it can be tested by obtaining more data, often by experimentation.
What is a hypothesis?
How can I improve my hypothesis?
How do I test my hypothesis?
Test it by coming up with an experiment to try to answer your hypothesis/question. For example, if you said, "if I made a wide winged airplane, it would go faster than a narrow winged airplane." Then you would build both and see which one went faster.
Can I use completed research to formulate a new hypothesis?
Yes, if the research applies to the hypothesis. Example: you have done an experiment to test why plants grow the way they grow,and have taken qualitative and quantitative observations. At the end of the experiment you find that your hypothesis was incorrect, so you have create a new one, but you don't have the time to do another experiment for your new hypothesis. Instead of completely redoing the whole thing and praying that there's still time left before the deadline, you can use the notes you already have to come up with a hypothesis that can be proven by the data resulting from the experiment.
Is there a maximum number of hypothesis that is allowed in one research paper?
There's not a strict limit, but your project or paper needs to be understandable and easily digestible, so you don't want to overwhelm the reader with too many experiments and proposals. It's best to limit each experiment to between one and four hypotheses, roughly.
How do I phrase the hypothesis when there are more than two IVs?
If both of the IVs support the result, then simply combine them using a conjunction like "and." For example, you could say, "If [this] and [that] occurs, then [result] will occur." If the two IVs change different DVs, then two separate hypotheses are required.
Ask a Question
- When examining the literature, look for research that is similar to what you want to do, and try to build on the findings of other researchers. But also look for claims that you think are suspicious, and test them yourself.
- Be specific in your hypotheses, but not so specific that your hypothesis can't be applied to anything outside your specific experiment. You definitely want to be clear about the population about which you are interested in drawing conclusions, but nobody (except your roommates) will be interested in reading a paper with the prediction: "my three roommates will each be able to do a different amount of pushups."
- Keep your feelings and opinions out of your research. Hypotheses should never say "I believe...," "I think...," "I feel...," or "My opinion is that...."
- Remember that science is not necessarily a linear process and can be approached in various ways.From the University of California at Berkeley's Understanding Science Website
What is a Hypothesis?
A hypothesis is a tentative, testable answer to a scientific question. Once a scientist has a scientific question she is interested in, the scientist reads up to find out what is already known on the topic. Then she uses that information to form a tentative answer to her scientific question. Sometimes people refer to the tentative answer as "an educated guess." Keep in mind, though, that the hypothesis also has to be testable since the next step is to do an experiment to determine whether or not the hypothesis is right!
A hypothesis leads to one or more predictions that can be tested by experimenting.
Predictions often take the shape of "If ____then ____" statements, but do not have to. Predictions should include both an independent variable (the factor you change in an experiment) and a dependent variable (the factor you observe or measure in an experiment). A single hypothesis can lead to multiple predictions, but generally, one or two predictions is enough to tackle for a science fair project.
Examples of Hypotheses and Predictions
|How does the size of a dog affect how much food it eats?||Larger animals of the same species expend more energy than smaller animals of the same type. To get the energy their bodies need, the larger animals eat more food.||If I let a 70-pound dog and a 30-pound dog eat as much food as they want, then the 70-pound dog will eat more than the 30-pound dog.|
|Does fertilizer make a plant grow bigger?||Plants need many types of nutrients to grow. Fertilizer adds those nutrients to the soil, thus allowing plants to grow more.||If I add fertilizer to the soil of some tomato seedlings, but not others, then the seedlings that got fertilizer will grow taller and have more leaves than the non-fertilized ones.|
|Does an electric motor turn faster if you increase the current?||Electric motors work because they have electromagnets inside them, which push/pull on permanent magnets and make the motor spin. As more current flows through the motor's electromagnet, the strength of the magnetic field increases, thus turning the motor faster.||If I increase the current supplied to an electric motor, then the RPMs (revolutions per minute) of the motor will increase.|
|Is a classroom noisier when the teacher leaves the room?||Teachers have rules about when to talk in the classroom. If they leave the classroom, the students feel free to break the rules and talk more, making the room nosier.||If I measure the noise level in a classroom when a teacher is in it and when she leaves the room, then I will see that the noise level is higher when my teacher is not in my classroom.|
What if My Hypothesis is Wrong?
What happens if, at the end of your science project, you look at the data you have collected and you realize it does not support your hypothesis? First, do not panic! The point of a science project is not to prove your hypothesis right. The point is to understand more about how the natural world works. Or, as it is sometimes put, to find out the scientific truth. When scientists do an experiment, they very often have data that shows their starting hypothesis was wrong. Why? Well, the natural world is complex—it takes a lot of experimenting to figure out how it works—and the more explanations you test, the closer you get to figuring out the truth. For scientists, disproving a hypothesis still means they gained important information, and they can use that information to make their next hypothesis even better. In a science fair setting, judges can be just as impressed by projects that start out with a faulty hypothesis; what matters more is whether you understood your science fair project, had a well-controlled experiment, and have ideas about what you would do next to improve your project if you had more time. You can read more about a science fair judge's view on disproving your hypothesis here.
It is worth noting, scientists never talk about their hypothesis being "right" or "wrong." Instead, they say that their data "supports" or "does not support" their hypothesis. This goes back to the point that nature is complex—so complex that it takes more than a single experiment to figure it all out because a single experiment could give you misleading data. For example, let us say that you hypothesize that earthworms do not exist in places that have very cold winters because it is too cold for them to survive. You then predict that you will find earthworms in the dirt in Florida, which has warm winters, but not Alaska, which has cold winters. When you go and dig a 3-foot by 3-foot-wide and 1-foot-deep hole in the dirt in those two states, you discover Floridian earthworms, but not Alaskan ones. So, was your hypothesis right? Well, your data "supported" your hypothesis, but your experiment did not cover that much ground. Can you really be sure there are no earthworms in Alaska? No. Which is why scientists only support (or not) their hypothesis with data, rather than proving them. And for the curious, yes there are earthworms in Alaska.
|What Makes a Good Hypothesis?||For a Good Hypothesis, You Should Answer "Yes" to Every Question|
|Is the hypothesis based on information from reference materials about the topic?||Yes / No|
|Can at least one clear prediction be made from the hypothesis?||Yes / No|
|Are predictions resulting from the hypothesis testable in an experiment?||Yes / No|
|Does the prediction have both an independent variable (something you change) and a dependent variable (something you observe or measure)?||Yes / No|
Educator Tools for Teaching about Hypotheses
Using our Google Classroom Integration, educators can assign a quiz to test student understanding of hypotheses. Educators can also assign students an online submission form to fill out detailing the hypothesis of their science project.
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