Nonparametrics Statistics Notes - Kolmogorov-Smirnov Two-Sample Test

The Kolmogorov-Smirnov test is another nonparametric alternative to the t-test for independent samples. Statistica expects the data to be arranged in the same way as for the t-test for independent samples. Specifically, the data file should contain a coding variable (independent variable) with at least two distinct codes that uniquely identify the group membership of each case in the data file. Select Comparing two independent samples (groups) on the Nonparametrics Statistics Startup Panel - Quick tab to display the Comparing Two Groups dialog box, in which you select the coding variable and a dependent variable list (variables on which the two groups are to be compared), and the codes used in the coding variable for identifying the two groups.

Assumptions and interpretation
The Kolmogorov-Smirnov test assesses the hypothesis that two samples were drawn from different populations. Unlike the parametric t-test for independent samples or the Mann-Whitney U test, which test for differences in the location of two samples (differences in means, differences in average ranks, respectively), the Kolmogorov-Smirnov test is also sensitive to differences in the general shapes of the distributions in the two samples (i.e., to differences in dispersion, skewness, etc.). Thus, its interpretation is similar to that of the Wald-Wolfowitz runs test.

See Comparing Two Groups - Quick tab for further details.