Hypothesis Testing Overview

Hypothesis development in scientific research is important because the process refines and focuses research by excluding extraneous variables and permitting variables to be quantified.

Scientists rarely begin a research study without a problem or a question to test. Without research questions or hypotheses, research proves to be a waste of time.

Researchers develop studies based on existing theory and are thus able to make predictions about the outcome of their work. Therefore, hypothesis development is usually the culmination of a rigorous literature review (we don't have the time to conduct a full-scale literature review in this class, so our theories will be based on our own experiences).

Researchers should use hypotheses in scientific research to:

1) provide direction for a study;
2) eliminate trial-and-error research;
3) rule out intervening and confounding variables; and,
4) allow for quantification of variables.

In addition, hypotheses should be:

1) compatible with current knowledge in the area;
2) logically consistent;
3) stated concisely; and,
4) testable.


In hypothesis testing, a researcher either rejects or fails to reject the null hypothesis that the statistical differences being analyzed are due to chance or random error.

To determine the statistical significance of a research study, the research must set a probability level (significance level) against which the null hypothesis is tested. If the results of the study indicate a probability lower than this level, the researcher can reject the null hypothesis. If the research outcome has a high probability, the researcher fails to reject the null hypothesis. It is common practice in mass media research studies to set the probability level at .05, which means that either one or five times out of 100, significant results of the study occur because of random error or chance. Another way to think of this is to say that "we are 95% confident that our results are not due to chance."

All research contains error. Typically, two types of error (Type I error: the rejection of a null hypothesis that should not be rejected, and Type II error: the acceptance of a null hypothesis that should be rejected) are relevant to hypothesis testing.

There is always the possibility of making an error in rejecting or failing to reject a null hypothesis. It is not easy for researchers to balance these two error types, but one procedure, power analysis, helps researchers deal with the problem. Because power (the probability of rejecting the null hypothesis when it is true) indicates the probability that a statistical test of a null hypothesis will result in the conclusion that the phenomenon under study actually exists, if there is a difference, researchers are able to detect it.