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| Solidny test Friedmana× | Robustowy test Kruskala-Wallisa× | |
|---|---|---|
| Dziedzina | Statystyka | Statystyka |
| Rodzina | Hypothesis test | Hypothesis test |
| Rok powstania≠ | 1990s–2000s | 1952 (base); robust variants 1990s–2000s |
| Twórca≠ | Extension of Friedman (1937); robust variants developed by Wilcox and colleagues | Kruskal & Wallis (1952); robust extensions by Wilcox and others |
| Typ≠ | Robust nonparametric repeated measures comparison | Nonparametric robust rank-based test |
| Źródło pierwotne≠ | Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838 | Mielke, P. W., & Berry, K. J. (2007). Permutation Methods: A Distance Function Approach (2nd ed.). Springer. ISBN: 978-0387698137 |
| Inne nazwy | robust rank-based repeated measures test, trimmed-mean Friedman test, Friedman test with robust estimation, Fried-type robust test | robust K-W test, trimmed Kruskal-Wallis, robust nonparametric one-way test, robust rank-based ANOVA |
| Pokrewne≠ | 6 | 3 |
| Podsumowanie≠ | The robust Friedman test is a nonparametric procedure for comparing three or more related (within-subjects) conditions that replaces standard ranking or mean-based summaries with robust location estimates — typically trimmed means or Winsorized statistics — to reduce the influence of outliers and heavy-tailed distributions on the inference. | The robust Kruskal-Wallis test is a nonparametric, rank-based method for comparing three or more independent groups when data contain outliers, heavy tails, or heterogeneous spread. It augments the classical Kruskal-Wallis H statistic with robust techniques — such as trimmed means on ranks or permutation-based inference — to maintain valid Type I error rates even when distributional assumptions are violated. |
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