What is the non-parametric test for more than two dependent samples?

What is the non-parametric test for more than two dependent samples?

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

Answer: (A)

For more than two related (dependent) samples, use the Friedmann test. It’s the non-parametric counterpart to a repeated measures ANOVA, designed when you have several conditions measured on the same participants and the data aren’t normally distributed or are ordinal. The test works by ranking the scores within each participant across the different conditions, then comparing the sums of those ranks to see if there are systematic differences across conditions. Because it relies on ranks rather than raw values, it doesn’t assume normality and is appropriate for small samples or skewed data. If there were more than two independent groups, the non-parametric alternative would be Kruskal-Wallis. If you had exactly two related samples, you’d use the Wilcoxon signed-rank test. If you had two independent samples, you’d use the Mann-Whitney U test.

For more than two related (dependent) samples, use the Friedmann test. It’s the non-parametric counterpart to a repeated measures ANOVA, designed when you have several conditions measured on the same participants and the data aren’t normally distributed or are ordinal. The test works by ranking the scores within each participant across the different conditions, then comparing the sums of those ranks to see if there are systematic differences across conditions. Because it relies on ranks rather than raw values, it doesn’t assume normality and is appropriate for small samples or skewed data.

If there were more than two independent groups, the non-parametric alternative would be Kruskal-Wallis. If you had exactly two related samples, you’d use the Wilcoxon signed-rank test. If you had two independent samples, you’d use the Mann-Whitney U test.