Development of the Research Hypothesis and Types of Hypothesis


The hypothesis is directly related to a theory but contains operationally defined variables and is in testable form.  Hypotheses allow us to determine, through research, if our theory is correct.  In other words, does prior work experience result in better grades?  When doing research, we are typically looking for some type of difference or change between two or more groups.  In our study, we are testing the difference between having work experience and not having work experience on college grades.  Every study has two hypotheses; one stated as a difference between groups and one stated as no difference between groups.

When stated as a difference between groups, our hypothesis would be, “students with prior work experience earn higher grades than students without prior work experience.”  This is called our research or scientific hypothesis.  Because most statistics test for no difference, however, we must also have a null hypothesis.  The null hypothesis is always written with the assumption that the groups do not differ.  In this study, our null hypothesis would state that, “students with work experience will not receive different grades than students with no work experience.”


Before scientists can begin working on a question that interests them, they need to formulate a research hypothesis. This is an important step in the scientific method because it determines the direction of the study. Scientists need to scrutinize previous work in the area and select an experimental design to use that helps them find data that either supports or rejects their hypothesis. Research hypotheses are of four types: null, directional, non-directional and causal.

  • Null Hypothesis

This is the conventional approach to making a prediction. It involves a statement that says there is no relationship between two groups that the researcher compares on a certain variable. The hypothesis also may state that there is no significant difference when different groups are compared with respect to a particular variable. For example, “There is no difference in the academic performance of high school students who participate in extracurricular activities and those who do not participate in such activities” is a null hypothesis. In many cases, the purpose of a null hypothesis is to allow the experimental results to contradict the hypothesis and prove the point that there is a definite relationship.

  • Non-directional Hypothesis

Certain hypothesis statements convey a relationship between the variables that the researcher compares, but do not specify the exact nature of this relationship. This form of hypothesis is used in studies where there is no sufficient past research on which to base a prediction. Continuing with the same example, a non directional hypothesis would read, “The academic performance of high school students is related to their participation in extracurricular activities.”

  • Directional Hypothesis

This type of hypothesis suggests the outcome the investigator expects at the end of the study. Scientific journal articles generally use this form of hypothesis. The investigator bases this hypothesis on the trends apparent from previous research on this topic. Considering the previous example, a researcher may state the hypothesis as, “High school students who participate in extracurricular activities have a lower GPA than those who do not participate in such activities.” Such hypotheses provide a definite direction to the prediction.

  • Causal Hypothesis

Some studies involve a measurement of the degree of influence of one variable on another. In such cases, the researcher states the hypothesis in terms of the effect of variations in a particular factor on another factor. This causal hypothesis is said to be bivariate because it specifies two aspects — the cause and the effect. For the example mentioned, the causal hypothesis will state, “High school students who participate in extracurricular activities spend less time studying which leads to a lower GPA.” When verifying such hypotheses, the researcher needs to use statistical techniques to demonstrate the presence of a relationship between the cause and effect. Such hypotheses also need the researcher to rule out the possibility that the effect is a result of a cause other than what the study has examined.

  • Complex Hypothesis

Complex hypothesis is that one in which as relationship among variables exists. I recommend you should read characteristics of a good research hypothesis. In this type dependent as well as independent variables are more than two. For example

Smoking and other drugs leads to cancer, tension chest infections etc.

The higher ration of unemployment poverty, illiteracy leads to crimes like dacoit, Robbery, Rape, prostitution & killing etc.

  • Empirical Hypothesis

Working hypothesis is that one which is applied to a field. During the formulation it is an assumption only but when it is pat to a test become an empirical or working hypothesis.

  • Logical Hypothesis

It is that type in which hypothesis is verified logically. J.S. Mill has given four cannons of these hypothesis e.g. agreement, disagreement, difference and residue.

  • Statistical Hypothesis

A hypothesis which can be verified statistically called statistical hypothesis. The statement would be logical or illogical but if statistic verifies it, it will be statistical hypothesis.

Error In Hypothesis

  • Type I Error
  • Type II Error


What is a Type I Error?

A Type I error (sometimes called a Type 1 error), is the incorrect rejection of a true null hypothesis. The alpha symbol, α, is usually used to denote a Type I error.

The Null Hypothesis in Type I and Type II Errors.

The null hypothesis, H0 is a commonly accepted hypothesis; it is the opposite of the alternate hypothesis. Researchers come up with an alternate hypothesis, one that they think explains a phenomenon, and then work to reject the null hypothesis. If that sounds a little convoluted, an example might help. Back in the day (way back!) scientists thought that the Earth was at the center of the Universe. That mean everything else — the sun, the planets, the whole shebang, all of those celestial bodies revolved around the Earth.

This Geocentric model has, of course, since been proven false. So the current, accepted hypothesis (the null) is:

H0: The Earth IS NOT at the center of the Universe

And the alternate hypothesis (the challenge to the null hypothesis) would be:

H1: The Earth IS at the center of the Universe.

What is a Type II Error?

A Type II error (sometimes called a Type 2 error) is the failure to reject a false null hypothesis. The probability of a type II error is denoted by the beta symbol β.

Type II Error: The Null Hypothesis in Action

Let’s say you’re an urban legend researcher and you want to research if people believe in urban legends like:

Newton was hit by an apple (he wasn’t).

Walt Disney drew Mickey mouse (he didn’t — Ub Werks did).

Marie Antoinette said “Let them eat cake” (she didn’t).

The accepted fact is, most people probably believe in urban legends (or we wouldn’t need*. So, your null hypothesis is:

H0: Most people do believe in urban legends.

But let’s say that null hypothesis is completely wrong. It might have been true ten years ago, but with the advent of the Smartphone — we have and at our fingertips. Still, your job as a researcher is to try and disprove the null hypothesis. So you come up with an alternate hypothesis:

H1: Most people DO NOT believe in urban legends.

You conduct your research by polling local residents at a retirement community and to your surprise you find out that most people do believe in urban legends. The problem is, you didn’t account for the fact that your sampling method introduced some bias…retired folks are less likely to have access to tools like Smartphones than the general population. So you incorrectly fail to reject the false null hypothesis that most people do believe in urban legends (in other words, most people do not, and you failed to prove that). You’ve committed an egregious Type II error, the penalty for which is banishment from the scientific community.

*I used this simple statement as an example of Type I and Type II errors. I haven’t actually researched this statement, so as well as committing numerous errors myself, I’m probably also guilty of sloppy science!

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