Non-parametric (or distribution-free) inferential statistical methods are mathematical procedures for statistical hypothesis testing which, unlike parametric statistics, make no assumptions about the frequency distributions of the variables being assessed. The most widely used of these methods is probably the chi-square test. Other widely used non-parametric methods include:

• binomial test
• Anderson-Darling test
• Cochran's Q
• Cohen's kappa
• Fisher's exact test
• Friedman two-way analysis of variance by ranks
• Kendall's W
• Kolmogorov-Smirnov test
• Kruskal-Wallis one-way analysis of variance by ranks
• Kuiper's test
• Mann-Whitney U or Wilcoxon rank sum test
• McNemar test (a special case of the chi-squared test)
• median test
• Siegel-Tukey test
• Spearman's rank correlation coefficient
• Wald-Wolfowitz runs test
• Wilcoxon matched pairs signed rank test

Nonparametric tests may have more statistical power than a parametric test when the assumptions underlying the parametric test are not satisfied.

See also: parametric statistics.

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