Which statement about non-parametric tests is true?

Which statement about non-parametric tests is true?

• A. They rely on the assumption of normal distribution.
• B. They require interval data.
• C. They do not rely on restrictive parametric assumptions and do not assume normal distribution.
• D. They are only used for categorical data.

Answer: (C)

Non-parametric tests are distribution-free; they don’t rely on the data being normally distributed and they don’t require interval data. They’re built on ranks or categories, which makes them flexible when data are ordinal, skewed, or otherwise not suited to parametric methods. That’s why this statement is true: they avoid restrictive parametric assumptions and do not assume a normal distribution. In contrast, normality, interval-scale requirements, or being limited to strictly categorical data aren’t necessary for non-parametric tests. They can handle ordinal data and many kinds of categorical data, and are especially useful with small samples or non-normal data.

Non-parametric tests are distribution-free; they don’t rely on the data being normally distributed and they don’t require interval data. They’re built on ranks or categories, which makes them flexible when data are ordinal, skewed, or otherwise not suited to parametric methods. That’s why this statement is true: they avoid restrictive parametric assumptions and do not assume a normal distribution. In contrast, normality, interval-scale requirements, or being limited to strictly categorical data aren’t necessary for non-parametric tests. They can handle ordinal data and many kinds of categorical data, and are especially useful with small samples or non-normal data.