Nonparametric tests, unlike parametric tests, do not imply any data assumptions. Nonparametric tests are commonly used under conditions where assumptions are violated or the required minimum sample size is not attained. The scale of measurement also can determine whether a parametric or nonparametric test is appropriate. The most common nonparametric tests are the chi-square test, two-way contingency analysis (a form of chi-square), and Mann-Whitney U-test, just to name a few.
To prepare for this Discussion, review Lesson 41 in the Green and Salkind (2017) text. Consider the impact of data analysis when the data distribution does not meet expected assumptions. Also consider conditions in which a nonparametric test is the most appropriate test.
By Day 3
Post an analysis of the relationship between data assumption violations and nonparametric analyses. In your analysis, do the following:
Compare the similarities and differences of parametric and nonparametric analyses in the context of data assumptions.
Provide at least one example of a parametric statistical test and its nonparametric equivalent, and explain how these examples illustrate the comparison of the two types of analysis.
Explain conditions under which you would use a nonparametric test (e.g., Mann-Whitney U-test over the independent samples t-test), including supportive examples from the course Resources for your explanation.
