Python statistical ecosystem is comprised of multiple packages. However, it still has numerous gaps and is surpassed by R packages and capabilities.

SciPy (version 1.2.0) offers Student, Wilcoxon, and Mann-Whitney tests which are not adapted to multiple pairwise comparisons. Statsmodels (version 0.9.0) features TukeyHSD test which needs some extra actions to be fluently integrated into a data analysis pipeline. Statsmodels also has good helper methods: allpairtest (adapts an external function such as scipy.stats.ttest_ind to multiple pairwise comparisons) and multipletests (adjusts p values to minimize type I and II errors). PMCMRplus is a very good R package which has no rivals in Python as it offers more than 40 various tests (including post hoc tests) for factorial and block design data. PMCMRplus was an inspiration and a reference for scikit-posthocs.

scikit-posthocs attempts to improve Python statistical capabilities by offering a lot of parametric and nonparametric post hoc tests along with outliers detection and basic plotting methods.


  • Omnibox tests:

    • Durbin test (for balanced incomplete block design).

  • Parametric pairwise multiple comparisons tests:

    • Scheffe test.

    • Student T test.

    • Tamhane T2 test.

    • TukeyHSD test.

  • Non-parametric tests for factorial design:

    • Conover test.

    • Dunn test.

    • Dwass, Steel, Critchlow, and Fligner test.

    • Mann-Whitney test.

    • Nashimoto and Wright (NPM) test.

    • Nemenyi test.

    • van Waerden test.

    • Wilcoxon test.

  • Non-parametric tests for block design:

    • Conover test.

    • Durbin and Conover test.

    • Miller test.

    • Nemenyi test.

    • Quade test.

    • Siegel test.

  • Other tests:

    • Anderson-Darling test.

    • Mack-Wolfe test.

    • Hayter (OSRT) test.

  • Outliers detection tests:

    • Simple test based on interquartile range (IQR).

    • Grubbs test.

    • Tietjen-Moore test.

    • Generalized Extreme Studentized Deviate test (ESD test).

  • Plotting functionality:

    • Significance plots.

    • Critical difference diagrams.

All post hoc tests are capable of p value adjustments for multiple pairwise comparisons.