What is good about non-parametric statistics tests?

What is good about non-parametric statistics tests?

• A. they are distribution free
• B. They require normal distribution.
• C. They rely on parametric assumptions.
• D. They have more power than parametric tests.

Answer: (A)

Non-parametric tests are distribution-free, meaning they don’t assume a specific population distribution for the data. They often work with ranks or medians rather than the actual values, which makes them robust to skew, outliers, and data on an ordinal or non-interval scale. This flexibility is especially helpful when normality or other parametric assumptions aren’t met, or when you have a small sample. They avoid relying on those distributional assumptions, but they aren’t inherently more powerful than parametric tests; in fact, when parametric assumptions are satisfied, parametric tests typically have more power.

Non-parametric tests are distribution-free, meaning they don’t assume a specific population distribution for the data. They often work with ranks or medians rather than the actual values, which makes them robust to skew, outliers, and data on an ordinal or non-interval scale. This flexibility is especially helpful when normality or other parametric assumptions aren’t met, or when you have a small sample. They avoid relying on those distributional assumptions, but they aren’t inherently more powerful than parametric tests; in fact, when parametric assumptions are satisfied, parametric tests typically have more power.