Which statement best describes parametric methods vs nonparametric methods?

Which statement best describes parametric methods vs nonparametric methods?

• A. Parametric methods require larger samples.
• B. Nonparametric methods make fewer assumptions and can be more robust.
• C. Parametric methods assume a specific distribution and often known parameters; nonparametric methods make fewer assumptions and can be more robust.
• D. Nonparametric methods always provide exact parameters.

Answer: (C)

This question tests understanding of how parametric and nonparametric methods differ in their assumptions and robustness. Parametric methods assume a specific form for the underlying distribution (for example, normal) and typically rely on estimating particular parameters like the mean and variance. Nonparametric methods avoid fixing a distribution shape and instead use more flexible approaches such as ranks or other non-distribution-based summaries. Because they make fewer assumptions, nonparametric methods can be more robust when the true distribution deviates from the assumed form. That’s why the statement describing parametric methods as assuming a specific distribution and often known parameters, while nonparametric methods make fewer assumptions and can be more robust, is the best description. Notes on the other options: saying parametric methods require larger samples isn’t a universal rule and depends on the method and context; nonparametric methods do not typically provide exact distribution parameters, since they do not assume a specific distribution to begin with.

This question tests understanding of how parametric and nonparametric methods differ in their assumptions and robustness. Parametric methods assume a specific form for the underlying distribution (for example, normal) and typically rely on estimating particular parameters like the mean and variance. Nonparametric methods avoid fixing a distribution shape and instead use more flexible approaches such as ranks or other non-distribution-based summaries. Because they make fewer assumptions, nonparametric methods can be more robust when the true distribution deviates from the assumed form.

That’s why the statement describing parametric methods as assuming a specific distribution and often known parameters, while nonparametric methods make fewer assumptions and can be more robust, is the best description.

Notes on the other options: saying parametric methods require larger samples isn’t a universal rule and depends on the method and context; nonparametric methods do not typically provide exact distribution parameters, since they do not assume a specific distribution to begin with.