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Analysis checklist: Kolmogorov-Smirnov test

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The Kolmogorov-Smirnov test is a nonparametric test that compares the distributions of two unmatched groups.

Are the values independent?

The results of a Kolmogorov-Smirnov test only make sense when the scatter is random – that whatever factor caused a value to be too high or too low affects only that one value. Prism cannot test this assumption. You must think about the experimental design. For example, the values are not independent if you have six values in each group, but these were obtained from two animals in each group (in triplicate). In this case, some factor may cause all triplicates from one animal to be high or low.

Are the data unpaired?

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.

Are you comparing exactly two groups?

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.

Are the data sampled from non-Gaussian populations?

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.

Have you entered raw data f(and not frequency distributions)?

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.


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