non-parametric statistics

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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