Which statement correctly describes a nonparametric test's data requirements?

• A. It requires the data to be normally distributed.
• B. It can only be used with interval data.
• C. It does not assume a specific distribution of the data.
• D. It always has greater power than parametric tests.

Answer: (C)

Nonparametric tests are distribution-free; they do not assume a specific shape for the population distribution. This makes them useful when data are not normally distributed, when sample sizes are small, or when measurements are ordinal because these tests rely on ranks or order rather than precise values. Because of that, they do not require normality or interval-level data. The key takeaway is that a nonparametric test does not assume a particular distribution for the data. Keep in mind they aren’t guaranteed to have more power than parametric tests; they’re often less powerful when parametric assumptions hold, but they’re more robust when those assumptions are violated.