The non-parametric test for comparing two independent groups is the Mann-Whitney U.

The non-parametric test for comparing two independent groups is the Mann-Whitney U.

• A. True
• B. False
• C. Not sure
• D. Both

Answer: (A)

When you’re comparing two independent groups and you can’t rely on the data being normally distributed, the Mann-Whitney U test is the go-to non-parametric method. It doesn’t require normality or equal variances and is suitable for ordinal data or continuous data with outliers that violate parametric assumptions. Conceptually, it asks whether the two groups tend to have different values, not by comparing means but by looking at the overall ordering of all observations. You replace each observation with its rank in the combined sample, then sum the ranks within each group. The resulting U statistic (and its distribution under the null hypothesis of identical distributions) tells you whether one group tends to yield higher values than the other. A significant result suggests a shift in the distribution between groups. It’s also useful to know that this test is often called the Wilcoxon rank-sum test. If the data come from paired or matched observations, you’d use the Wilcoxon signed-rank test instead. And for more than two groups, you’d turn to the Kruskal-Wallis test, which is a generalization of this non-parametric approach. In short, the statement is correct because Mann-Whitney U is the standard non-parametric method for comparing two independent groups when normality cannot be assumed.

When you’re comparing two independent groups and you can’t rely on the data being normally distributed, the Mann-Whitney U test is the go-to non-parametric method. It doesn’t require normality or equal variances and is suitable for ordinal data or continuous data with outliers that violate parametric assumptions.

Conceptually, it asks whether the two groups tend to have different values, not by comparing means but by looking at the overall ordering of all observations. You replace each observation with its rank in the combined sample, then sum the ranks within each group. The resulting U statistic (and its distribution under the null hypothesis of identical distributions) tells you whether one group tends to yield higher values than the other. A significant result suggests a shift in the distribution between groups.

It’s also useful to know that this test is often called the Wilcoxon rank-sum test. If the data come from paired or matched observations, you’d use the Wilcoxon signed-rank test instead. And for more than two groups, you’d turn to the Kruskal-Wallis test, which is a generalization of this non-parametric approach.

In short, the statement is correct because Mann-Whitney U is the standard non-parametric method for comparing two independent groups when normality cannot be assumed.