The non-parametric alternative to a one-way ANOVA is Friedman's ANOVA.

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Multiple Choice

The non-parametric alternative to a one-way ANOVA is Friedman's ANOVA.

Explanation:
The main idea is that when you’re comparing more than two conditions and the measurements come from the same subjects (a one-factor repeated-measures design) but the data don’t meet parametric assumptions, Friedman's test is the nonparametric analogue of the one-way ANOVA. It works by ranking each subject’s scores across the conditions, then comparing the sum of ranks across conditions to what would be expected if there were no differences. The result is evaluated with a chi-square distribution with degrees of freedom equal to the number of conditions minus one. So, the statement is true in this within-subjects context: Friedman's ANOVA serves as the nonparametric substitute for a one-way ANOVA. If instead you had independent groups (between-subjects design), the appropriate nonparametric counterpart would be Kruskal-Wallis, not Friedman's.

The main idea is that when you’re comparing more than two conditions and the measurements come from the same subjects (a one-factor repeated-measures design) but the data don’t meet parametric assumptions, Friedman's test is the nonparametric analogue of the one-way ANOVA. It works by ranking each subject’s scores across the conditions, then comparing the sum of ranks across conditions to what would be expected if there were no differences. The result is evaluated with a chi-square distribution with degrees of freedom equal to the number of conditions minus one.

So, the statement is true in this within-subjects context: Friedman's ANOVA serves as the nonparametric substitute for a one-way ANOVA. If instead you had independent groups (between-subjects design), the appropriate nonparametric counterpart would be Kruskal-Wallis, not Friedman's.

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