Name a nonparametric test for two independent samples.

Name a nonparametric test for two independent samples.

• A. Mann-Whitney U test
• B. Paired t-test
• C. ANOVA
• D. Chi-squared test

Answer: (A)

When you want to compare two independent groups but can't assume that the data are normally distributed, you turn to a nonparametric approach that relies on the data’s ranks rather than their exact values. The Mann-Whitney U test is built for this scenario. It compares the overall ranking of all observations from both groups, not the raw means, so it doesn’t require normality or equal variances. Because it uses ranks, it’s valid for ordinal data as well as continuous data that violate normality, and it’s robust to outliers. This test specifically targets whether one group tends to have higher values than the other, which is the essence of comparing two independent samples without parametric assumptions. It’s the go-to alternative to the two-sample t-test when normality can’t be assumed. The other options aren’t suitable here for this goal: a paired t-test is for related or matched samples and assumes normality of the differences; ANOVA compares means across three or more groups and is a parametric method; the chi-squared test handles categorical data (counts) rather than numeric measurements.

When you want to compare two independent groups but can't assume that the data are normally distributed, you turn to a nonparametric approach that relies on the data’s ranks rather than their exact values. The Mann-Whitney U test is built for this scenario. It compares the overall ranking of all observations from both groups, not the raw means, so it doesn’t require normality or equal variances. Because it uses ranks, it’s valid for ordinal data as well as continuous data that violate normality, and it’s robust to outliers.

This test specifically targets whether one group tends to have higher values than the other, which is the essence of comparing two independent samples without parametric assumptions. It’s the go-to alternative to the two-sample t-test when normality can’t be assumed.

The other options aren’t suitable here for this goal: a paired t-test is for related or matched samples and assumes normality of the differences; ANOVA compares means across three or more groups and is a parametric method; the chi-squared test handles categorical data (counts) rather than numeric measurements.