Nonparametric tests differ from parametric tests in that they use ranks of data. They are said to have what power relative to parametric equivalents?

Nonparametric tests differ from parametric tests in that they use ranks of data. They are said to have what power relative to parametric equivalents?

• A. They have about 95.5% of the power of parametric tests.
• B. They have greater power than parametric tests.
• C. They have the same power as parametric tests.
• D. They have less power only with large samples.

Answer: (A)

Nonparametric tests use ranks instead of the actual data values, so they’re more robust to violations of normality and other assumptions. That rank-based approach tends to reduce the information each result provides, which generally lowers statistical power compared with parametric tests that use the raw data when the parametric assumptions hold. A commonly cited rule of thumb is that nonparametric tests have about 95% of the power of their parametric counterparts (roughly 95.5% in some formulations). In other words, you gain robustness at the cost of a modest drop in power. The exact amount of power loss depends on the distribution and the specific test, so the difference isn’t fixed in every situation. The other statements don’t reflect this usual trade-off: nonparametric tests aren’t typically more powerful, nor are they always exactly the same power, and the idea that the loss occurs only with large samples isn’t accurate.

Nonparametric tests use ranks instead of the actual data values, so they’re more robust to violations of normality and other assumptions. That rank-based approach tends to reduce the information each result provides, which generally lowers statistical power compared with parametric tests that use the raw data when the parametric assumptions hold. A commonly cited rule of thumb is that nonparametric tests have about 95% of the power of their parametric counterparts (roughly 95.5% in some formulations). In other words, you gain robustness at the cost of a modest drop in power. The exact amount of power loss depends on the distribution and the specific test, so the difference isn’t fixed in every situation. The other statements don’t reflect this usual trade-off: nonparametric tests aren’t typically more powerful, nor are they always exactly the same power, and the idea that the loss occurs only with large samples isn’t accurate.