How To Write A Hypothesis 13 Steps With Pictures

If you are writing a hypothesis for a school assignment, this step may be taken care of for you.

Focus on academic and scholarly writing. You need to be certain that your information is unbiased, accurate, and comprehensive. Scholarly search databases such as Google Scholar and Web of Science can help you find relevant articles from reputable sources. 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.

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” in scientific papers 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.

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.

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.

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.

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. "

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.

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. "

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.

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 a research hypothesis, a statement that includes the hypothesis, the experiment, and the prediction all in one statement. For example, 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.

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. 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. [2] X Research source