Which statement about parametric and nonparametric tests is accurate?

Which statement about parametric and nonparametric tests is accurate?

• A. Parametric tests rely on underlying parameters and assumptions; nonparametric tests use ranks and typically have about 95.5% of the power.
• B. Nonparametric tests rely on population parameters and have greater power than parametric tests.
• C. Parametric tests do not rely on assumptions about the data.
• D. Nonparametric tests require the same assumptions as parametric tests.

Answer: (A)

Parametric tests rely on estimating population parameters and on specific assumptions about the data, such as a particular distribution (often normal), measurement level, and independence. Nonparametric tests, in contrast, do not depend on those distributional assumptions; they work with ranks or medians, making them more robust when data don’t meet the strict requirements of parametric methods. This distinction is captured by the statement because it correctly notes that parametric tests depend on parameters and assumptions, while nonparametric tests use ranks and are less tied to a specific distribution. The part about power is not a universal rule—nonparametric methods generally have less power than parametric ones when the parametric assumptions hold, and power depends on the data and context. The other options misstate the relationships: nonparametric tests do not rely on population parameters; they do not universally have greater power; parametric tests do rely on assumptions; and nonparametric tests do not require the same assumptions as parametric tests.

Parametric tests rely on estimating population parameters and on specific assumptions about the data, such as a particular distribution (often normal), measurement level, and independence. Nonparametric tests, in contrast, do not depend on those distributional assumptions; they work with ranks or medians, making them more robust when data don’t meet the strict requirements of parametric methods.

This distinction is captured by the statement because it correctly notes that parametric tests depend on parameters and assumptions, while nonparametric tests use ranks and are less tied to a specific distribution. The part about power is not a universal rule—nonparametric methods generally have less power than parametric ones when the parametric assumptions hold, and power depends on the data and context.

The other options misstate the relationships: nonparametric tests do not rely on population parameters; they do not universally have greater power; parametric tests do rely on assumptions; and nonparametric tests do not require the same assumptions as parametric tests.