C. Hypothesis Testing

Generating and Testing Hypotheses

Generally speaking, in social science research, we establish two competing hypotheses that we then evaluate in light of some empirical data. These hypotheses are referred to as the null hypothesis and the alternative hypothesis. The primary purpose of hypothesis testing is to examine the likelihood of the null hypothesis with data.

The null hypothesis is often the reverse of what the experimenter actually believes; it is put forward to allow the data to contradict it. In the study of the effect of sense of control on health, the researchers expect that a sense of control will improve health. The null hypothesis they would establish in this setting, then, is that enhancing sense of control will have no effect on health. The alternative hypothesis is one that stands in contrast to the null, usually that the condition or change will have some effect. In the sense of control example, the alternative hypothesis is that changes in sense of control will result in a change in health.

Depending on the data, the null hypothesis either will or will not be rejected as a viable possibility. If the data show a sufficiently large effect of the sense of control, then the null hypothesis that sense of control has no effect can be rejected. Specific criteria used to accept or reject the null hypothesis are discussed in the modules describing statistical tests used to evaluate hypotheses.

With this understanding of the way that hypotheses are generated in the social sciences, you're ready to look at specific tools that are used to test hypotheses.