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Directional Hypothesis
A directional hypothesis is a prediction made by a researcher regarding a positive or negative change, relationship, or difference between two variables of a population. This prediction is typically based on past research, accepted theory, extensive experience, or literature on the topic. Key words that distinguish a directional hypothesis are: higher, lower, more, less, increase, decrease, positive, and negative. A researcher typically develops a directional hypothesis from research questions and uses statistical methods to check the validity of the hypothesis.
Examples of Directional Hypotheses
A general format of a directional hypothesis would be the following: For (Population A), (Independent Variable 1) will be higher than (Independent Variable 2) in terms of (Dependent Variable). For example, “For ninth graders in Central High School, test scores of Group 1 will be higher than test scores of Group 2 in terms of Group 1 receiving a specified treatment.” The following are other examples of directional hypotheses:
- There is a positive relationship between the number of books read by children and the children's scores on a reading test.
- Teenagers who attend tutoring sessions will make higher achievement test scores than comparable teenagers who do not attend tutoring sessions.
Nondirectional and Null Hypotheses
In order to fully understand a directional hypothesis, there must also be a clear understanding of a nondirectional hypothesis and null hypothesis.
Nondirectional Hypothesis
A nondirectional hypothesis differs from a directional hypothesis in that it predicts a change, relationship, or difference between two variables but does not specifically designate the change, relationship, or difference as being positive or negative. Another difference is the type of statistical test that is used. An example of a nondirectional hypothesis would be the following: For (Population A), there will be a difference between (Independent Variable 1) and (Independent Variable 2) in terms of (Dependent Variable 1). The following are other examples of nondirectional hypotheses:
- There is a relationship between the number of books read by children and the children's scores on a reading test.
- Teenagers who attend tutoring sessions will have achievement test scores that are significantly different from the scores of comparable teenagers who do not attend tutoring sessions.
Null Hypothesis
Statistical tests are not designed to test a directional hypothesis or nondirectional hypothesis, but rather a null hypothesis. A null hypothesis is a prediction that there will be no change, relationship, or difference between two variables. A null hypothesis is designated by Ho. An example of a null hypothesis would be the following: for (Population A), (Independent Variable 1) will not be different from (Independent Variable 2) in terms of (Dependent Variable). The following are other examples of null hypotheses:
- There is no relationship between the number of books read by children and the children's scores on a reading test.
- Teenagers who attend tutoring sessions will make achievement test scores that are equivalent to those of comparable teenagers who do not attend tutoring sessions.
Statistical Testing of Directional Hypothesis
A researcher starting with a directional hypothesis will have to develop a null hypothesis for the purpose for running statistical tests. The null hypothesis predicts that there will not be a change or relationship between variables of the two groups or populations. The null hypothesis is designated by H0, and a null hypothesis statement could be written as H0: μ1 = μ2 (Population or Group 1 equals Population or Group 2 in terms of the dependent variable). A directional hypothesis or nondirectional hypothesis would then be considered to be an alternative hypothesis to the null hypothesis and would be designated as H1. Since the directional hypothesis is predicting a direction of change or difference, it is designated as H1: μ1 > μ2 or H1: μ1 < μ2 (Population or Group 1 is greater than or less than Population or Group 2 in terms of the dependent variable). In the case of a nondirectional hypothesis, there would be no specified direction, and it could be designated as H1: μ1 ≠ μ2 (Population or Group 1 does not equal Population or Group 2 in terms of the dependent variable).
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