The Kolmogorov-Smirnov test is a nonparametric test that compares the distributions of two unmatched groups.
The Kolmogorov-Smirnov test works by comparing the cumulative frequency distributions of the two groups.It does not account for any matching or pairing. If the data are paired or matched, consider using a Wilcoxon matched pairs test instead.
Use the Kolmogorov-Smirnov test only to compare two groups. To compare three or more groups, use the Kruskal-Wallis test followed by post tests. It is not appropriate to perform several Kolmogorov-Smirnov tests, comparing two groups at a time without doing some correction for multiple comparisons.
By selecting a nonparametric test, you have avoided assuming that the data were sampled from Gaussian distributions, but there are drawbacks to using a nonparametric test. If the populations really are Gaussian, the nonparametric tests have less power (are less likely to give you a small P value), especially with small sample sizes.
The Kolmogorov-Smirnov test compares two cumulative frequency distributions. Prism creates these distributions from raw data. Prism cannot run the Kolmogorov-Smirnov test from distributions you enter, only from raw data entered into two columns of a Column data table.