Which non-parametric test serves as the counterpart to repeated measures ANOVA?

Which non-parametric test serves as the counterpart to repeated measures ANOVA?

• A. Friedman’s ANOVA
• B. Kruskal-Wallis
• C. Wilcoxon
• D. Mann-Whitney U

Answer: (A)

When you have more than two related (within-subjects) conditions and you can’t rely on normality, the non-parametric counterpart to repeated measures ANOVA is Friedman's ANOVA. It works by ranking each participant’s scores across the repeated measures and then testing whether those average ranks differ across the conditions. This approach respects the within-subject design and reduces the impact of non-normal data since it relies on ranks rather than raw values. The test statistic follows a chi-square distribution with degrees of freedom equal to the number of conditions minus one. This is different from Kruskal-Wallis, which compares three or more independent groups, and from the Wilcoxon signed-rank test, which handles just two related samples. Mann-Whitney U is for two independent groups. So Friedman's ANOVA is the appropriate non-parametric alternative when you’re dealing with more than two related samples.

When you have more than two related (within-subjects) conditions and you can’t rely on normality, the non-parametric counterpart to repeated measures ANOVA is Friedman's ANOVA. It works by ranking each participant’s scores across the repeated measures and then testing whether those average ranks differ across the conditions. This approach respects the within-subject design and reduces the impact of non-normal data since it relies on ranks rather than raw values. The test statistic follows a chi-square distribution with degrees of freedom equal to the number of conditions minus one.

This is different from Kruskal-Wallis, which compares three or more independent groups, and from the Wilcoxon signed-rank test, which handles just two related samples. Mann-Whitney U is for two independent groups. So Friedman's ANOVA is the appropriate non-parametric alternative when you’re dealing with more than two related samples.