What is a nonparametric test, and when might you use one?

What is a nonparametric test, and when might you use one?

• A. A test that assumes a normal distribution
• B. A test that does not assume a specific distribution and uses ranks
• C. A test that always requires interval data
• D. A test that only applies to categorical data

Answer: (B)

Nonparametric tests are distribution-free tests that do not assume a specific form for the population distribution. Instead of relying on means and variances, they work with the ranks of the data. This makes them robust to outliers and skewed shapes and suitable for ordinal data or small samples where normality can’t be assumed. You’d use one when the assumptions of parametric tests aren’t met—for example, when data are not normally distributed, when you have ordinal outcomes, or when outliers would distort a t-test. These tests focus on differences in location or overall distributions without prescribing a particular distribution shape. Common examples include using ranked tests for two independent groups, paired data, or more than two groups, such as Mann-Whitney U, Wilcoxon signed-rank, and Kruskal-Wallis tests.

Nonparametric tests are distribution-free tests that do not assume a specific form for the population distribution. Instead of relying on means and variances, they work with the ranks of the data. This makes them robust to outliers and skewed shapes and suitable for ordinal data or small samples where normality can’t be assumed. You’d use one when the assumptions of parametric tests aren’t met—for example, when data are not normally distributed, when you have ordinal outcomes, or when outliers would distort a t-test. These tests focus on differences in location or overall distributions without prescribing a particular distribution shape. Common examples include using ranked tests for two independent groups, paired data, or more than two groups, such as Mann-Whitney U, Wilcoxon signed-rank, and Kruskal-Wallis tests.