Kruskal-Wallis Test vs Friedman's ANOVA Test

 Kruskal-Wallis Test vs Friedman's ANOVA Test







Kruskal-Wallis Test


• Compares the medians of two or more independent groups.

• Non-parametric, does not require normality assumption.

• Can be used with ordinal or continuous data.

• Robust to outliers.


Friedman's ANOVA Test

• Compares the medians of multiple related samples.

• Non-parametric, does not require normality assumption.

• Can be used with ordinal or continuous data.

• Robust to outliers.


Key Differences:

• Independence of samples: Kruskal-Wallis test is used for independent samples, while Friedman's ANOVA test is used for related samples.

• Number of groups: Kruskal-Wallis test can be used for two or more groups, while Friedman's ANOVA test is specifically designed for three or more groups.


Which test to use:

• If you have independent samples and want to compare their medians, use the Kruskal-Wallis test.

• If you have related samples and want to compare their medians, use Friedman's ANOVA test.


Example:

• Kruskal-Wallis test: Comparing the median incomes of three different cities.

• Friedman's ANOVA test: Comparing the median test scores of students on three different versions of a test.

Note: Both tests are non-parametric and robust to outliers, making them useful for analyzing data that does not meet the assumptions of parametric tests like the one-way ANOVA.

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