When the parametric assumption on the distribution is met, a parametric test is usually more powerful than a nonparametric test.

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When the parametric assumption on the distribution is met, a parametric test can make use of additional information about the population parameters, such as the mean and standard deviation, leading to more precise and powerful tests. Nonparametric tests are typically used when parametric assumptions are violated, leading to less power. When the assumptions for a parametric test are met, it is more powerful than a nonparametric test because it can take advantage of the additional information provided by a known distribution. In contrast, nonparametric tests do not assume a specific distribution and therefore may have less statistical power.

When the Parametric Assumption on the Distribution Is Met, a Parametric