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Nonparametric Tests

Most types of hypothesis test require that the population conforms to a particular distribution, usually the normal distribution. Where this is not the case a nonparametric test can be used.

Nonparametric tests make no assumptions about the type of distribution (although they may require symmetry, or some other property). The disadvantage is that nonparametric tests are not as efficient; for a given data set the nonparametric test will give a higher p-value.

Nonparametric Tests include:

 Friedman's Test nonparametric alternative to two way ANOVA Kruskal Wallis Test nonparametric alternative to ANOVA Levene's Test tests for equality of variances of several samples Mann Whitney Test nonparametric alternative to two sample t-test Mood's Median Test nonparametric alternative to ANOVA Sign Scores Test alternative name for Mood's Median Test Wilcoxon Rank Sum Test same as the Mann Whitney Test Wilcoxon Signed Rank Test nonparametric equivalent to one sample t-test

Parametric tests include ANOVA, the t test, the F test and the chi-square test.

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