When would you use a nonparametric test like Mann-Whitney U instead of a two-sample t-test?

When would you use a nonparametric test like Mann-Whitney U instead of a two-sample t-test?

• A. When data are normally distributed with equal variances.
• B. When data are ordinal or not normally distributed.
• C. When sample sizes are extremely large.
• D. When the data set has fewer than five observations.

Answer: (B)

Choosing a nonparametric test like Mann-Whitney U is about how your data behave and what they’re measured on. The two-sample t-test relies on assumptions that the populations are roughly normally distributed and that the groups have similar variances, and it uses the actual values on a meaningful scale (typically interval or ratio). When the data are ordinal (ranked) or clearly not normally distributed, those assumptions don’t hold, and using ranks instead of raw values helps avoid misleading results. That’s why the Mann-Whitney U test is appropriate in that situation—it compares the central tendency by ranks and does not depend on normality or equal variances. So the best fit is when the data are ordinal or not normally distributed. If the data were truly normal with equal variances, the two-sample t-test would be the standard choice. Large samples don’t remove the need for appropriate assumptions, and with very small samples neither test is ideal, but the deciding factor here is the measurement level and distribution: ordinal or nonnormal data call for a nonparametric approach.

Choosing a nonparametric test like Mann-Whitney U is about how your data behave and what they’re measured on. The two-sample t-test relies on assumptions that the populations are roughly normally distributed and that the groups have similar variances, and it uses the actual values on a meaningful scale (typically interval or ratio). When the data are ordinal (ranked) or clearly not normally distributed, those assumptions don’t hold, and using ranks instead of raw values helps avoid misleading results. That’s why the Mann-Whitney U test is appropriate in that situation—it compares the central tendency by ranks and does not depend on normality or equal variances.

So the best fit is when the data are ordinal or not normally distributed. If the data were truly normal with equal variances, the two-sample t-test would be the standard choice. Large samples don’t remove the need for appropriate assumptions, and with very small samples neither test is ideal, but the deciding factor here is the measurement level and distribution: ordinal or nonnormal data call for a nonparametric approach.