Answer Both questions 1. In general, when should you use non-parametric vs. parametric tests? 2. Specifically,...

Answer Both questions

1. In general, when should you use non-parametric vs. parametric tests?

2. Specifically, what are the parametric equivalents of the Mann-Whitney, Wilcoxon rank-sum & signed-rank, Kruskal-Wallis, and Friedman's tests? what type of variables or research design would call for each of these tests (i.e. how do know which test to use)?

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Expert Solution

a. A general rule to use while deciding to use a non-paramedic versus a parametric test is to investigate whether the data is normally distributed. Nonparametric tests are sometimes called distribution-free tests because they are based on fewer assumptions. Parametric tests, on the other hand, assume that the data is approximately normally distributed. There are some situations when it is clear that the data does not follow a normal distribution. These include situations:

when the variable is an ordinal variable or a rank,
when there are definite outliers or
when the varible has clear limits of detection.

2. The parametric equivalents of the tests are stated as under:

Mann-Whitney: t-test for independent samples.
Wilcoxin rank-sum & signed-rank: t-test for dependent samples.
Kruskal-Wallis: One way ANOVA with independent measures.
Friedman's tests: One-way ANOVA with repeated measures.

Hayet Jeridi answered 1 month ago

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