What is the primary difference between parametric and nonparametric tests of significance?
A) Nonparametric tests are involved with populations that have no parameters.
B) Nonparametric tests involve only interval data, whereas parametric tests involve only ordinal data.
C) Nonparametric tests do not have hypotheses based on population parameters or assumptions based on the distributions of populations from which the samples are drawn.
D) Nonparametric tests do not make any distributional assumptions throughout the entire process.

Question

Quizplus · Accepted Answer

The primary difference between parametric and nonparametric tests of significance is that nonparametric tests do not have assumptions based on the distributions of populations from which the samples are drawn, nor do they have hypotheses based on population parameters. This means that nonparametric tests can be used when data do not meet the assumptions of parametric tests or when population parameters are unknown or not applicable, such as with nominal or ordinal data or when sample sizes are small. Parametric tests, on the other hand, require data to be normally distributed and have parameters that are known or assumable, such as with interval or ratio data. Therefore, C is the best choice. The other options are incorrect as they do not accurately describe the primary difference between parametric and nonparametric tests.

What Is the Primary Difference Between Parametric and Nonparametric Tests