What is the non-parametric equivalent of a one-way ANOVA?

What is the non-parametric equivalent of a one-way ANOVA?

• A. Kruskal-Wallis one-way analysis of variance
• B. Friedman test
• C. Mann-Whitney U test
• D. Spearman correlation

Answer: (A)

When you want to compare more than two independent groups without assuming normal distribution, you use a non-parametric approach that relies on ranks rather than raw values. The Kruskal-Wallis test does exactly this: it ranks all observations across all groups and tests whether the average ranks differ by group. If the groups have different distributions or central tendencies, the test will indicate a difference among at least one pair of groups. It’s appropriate for ordinal data or continuous data that violate normality or homogeneity of variance, extending the idea of comparing groups to multiple groups without relying on parametric assumptions. The other options don’t fit this specific scenario: the Friedman test is for related (repeated measures) designs, not independent groups; the Mann-Whitney U test compares just two independent groups; Spearman correlation assesses the strength of association between two variables, not differences across multiple groups.

When you want to compare more than two independent groups without assuming normal distribution, you use a non-parametric approach that relies on ranks rather than raw values. The Kruskal-Wallis test does exactly this: it ranks all observations across all groups and tests whether the average ranks differ by group. If the groups have different distributions or central tendencies, the test will indicate a difference among at least one pair of groups. It’s appropriate for ordinal data or continuous data that violate normality or homogeneity of variance, extending the idea of comparing groups to multiple groups without relying on parametric assumptions.

The other options don’t fit this specific scenario: the Friedman test is for related (repeated measures) designs, not independent groups; the Mann-Whitney U test compares just two independent groups; Spearman correlation assesses the strength of association between two variables, not differences across multiple groups.