A Kolmogorv-Smirnov test indicates whether the values of a numeric column tend to be distributed differently across two or more categories. Unlike an ANOVA test, it looks for differences in the entire distribution and not just the mean; however, the Kolmogorov-Smirnov test usually requires more observations than other tests in order to detect a difference.

To perform a Kolmogorov-Smirnov test:

- In the Summary view, use the confidence slider to choose a desired confidence level
- Choose the numeric column of interest on the left
- Choose a category column that takes two or more values in the Covariate table
- Click the button labeled
*Bars*(or*Dots*) below the Covariate view - The result of the Kolmogorov-Smirnov test will appear in the Bottom Line

See also:

- Performing a Kolmogorov-Smirnov test of two or more numeric columns’ distributions
- Adjusting a p-value to account for multiple comparisons
- Performing a Mann-Whitney test of a numeric column’s median across two categories
- Performing a Kruskal-Wallis test of a numeric column’s median across values of another columns
- Performing a t-test of a numeric column’s mean across two categories
- Performing an ANOVA test of a numeric column’s mean across values of another column
- Viewing the correlation coefficient between two numeric columns
- Formatting descriptive and inferential statistics
- About the Summary view