Difference between parametric and nonparametric methods

• A. Parametric methods assume distributional forms and parameters; nonparametric do not.
• B. Nonparametric methods assume distributional forms; parametric do not.
• C. Parametric methods require no distributional assumptions.
• D. Both require normality of the population.

Answer: (A)

Parametric methods assume a specific distributional form and estimate a finite set of parameters for that distribution (for example, a normal distribution described by its mean and standard deviation). Nonparametric methods do not assume a fixed distribution shape and are often based on ranks or other order-based features, offering more flexibility when the data don’t meet strict distributional assumptions. This contrast is why the statement that parametric methods require distributional forms and parameters is the best description. The other options misstate the relationship: nonparametric methods do not assume a distributional form, parametric methods do require distributional assumptions, and normality is not a universal requirement for all parametric methods nor is it required by nonparametric methods.