Nonparametric Statistics Button

Click the button to display the Nonparametric Statistics Startup Panel. Nonparametric statistics were developed for use in cases when the researcher does not know the parameters of the distribution of the variable of interest in the population (hence the name nonparametric). In more technical terms, nonparametric methods do not rely on the estimation of parameters (such as the mean or the standard deviation) describing the distribution of the variable of interest in the population. Therefore, these methods are also sometimes (and more appropriately) called parameter-free methods or distribution-free methods.

The Nonparametric Statistics module features a comprehensive selection of inferential and descriptive statistics including all common tests and some special application procedures. Available statistical procedures include the Wald-Wolfowitz runs test, Mann-Whitney U test (with exact probabilities for small samples), Kolmogorov-Smirnov tests, Wilcoxon matched pairs test, Kruskal-Wallis ANOVA by ranks, Median test, Sign test, Friedman ANOVA by ranks, Cochran Q test, McNemar test, Kendall coefficient of concordance, Kendall Tau (b, c), Spearman rank order R, Fisher's exact test, Chi-square tests, V-square statistic, Phi, Gamma, Somers' D, contingency coefficients, and others.

Specialized nonparametric tests and statistics are also part of many modules, e.g., Survival Analysis, STATISTICA Process Analysis, and others.