As long as the assumptions on which parametric statistics are, for the most part, met, the researcher uses a t test, ANOVA, ANCOVA, MANOVA, or some other parametric procedure. If these assumptions are not met—that is, if the data are not interval or ratio or are not distributed normally—the researcher should consider using a nonparametric analog to the parametric test. For most parametric procedures, a corresponding non-parametric test can be used. The interpretation of the results is similar with both kinds of tests. What differs is the computational equation and tables for determining the significance level of the results. Both procedures test a hypothesis and report a level of significance for rejecting the null. In contrast to parametric tests, however, nonparametric tests do not test hypotheses about characteristics of a population. Rather, nonparametric procedures test hypotheses about relationships between categorical variables, shapes of distributions, and normality of distribution. While parametric procedures use means, nonparametric techniques are concerned with frequencies, percentages, and proportions. The parametric tests are generally more powerful in detecting significant differences and are used frequently even when all assumptions cannot be met.
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