The Friedman test is the nonparametric alternative to which parametric test?

The Friedman test is the nonparametric alternative to which parametric test?

• A. Independent t-test
• B. One-way repeated measures ANOVA
• C. Two-way ANOVA
• D. Paired t-test

Answer: (B)

The main idea here is that the Friedman test serves as the nonparametric counterpart to the one-way repeated measures ANOVA. It is used when you have more than two related or matched conditions measured on the same subjects and you cannot assume that the data are normally distributed. Instead of using raw scores, the Friedman test works with ranks across those related conditions, preserving the within-subjects nature of the design while relaxing the normality assumption. This makes it the appropriate nonparametric alternative for detecting overall differences across multiple related conditions. The other options don’t fit because the independent t-test compares two unrelated groups, and the paired t-test compares two related conditions (only two levels). Two-way ANOVA involves two factors (and can include interactions), whereas Friedman handles a single within-subjects factor with multiple levels. For more than two related conditions with nonparametric data, Friedman is the right choice.

The main idea here is that the Friedman test serves as the nonparametric counterpart to the one-way repeated measures ANOVA. It is used when you have more than two related or matched conditions measured on the same subjects and you cannot assume that the data are normally distributed. Instead of using raw scores, the Friedman test works with ranks across those related conditions, preserving the within-subjects nature of the design while relaxing the normality assumption. This makes it the appropriate nonparametric alternative for detecting overall differences across multiple related conditions.

The other options don’t fit because the independent t-test compares two unrelated groups, and the paired t-test compares two related conditions (only two levels). Two-way ANOVA involves two factors (and can include interactions), whereas Friedman handles a single within-subjects factor with multiple levels. For more than two related conditions with nonparametric data, Friedman is the right choice.