Rejection of a hypothesis using a nonparametric test is more convincing than using an equivalent parametric test when the data are badly skewed.

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Nonparametric tests do not make any assumptions about the shape of the distribution, while parametric tests assume that the data are normally distributed. If the data are badly skewed, a nonparametric test may be more appropriate and rejecting a hypothesis with it would be more convincing since it is less susceptible to type I errors.

Rejection of a Hypothesis Using a Nonparametric Test Is More