Using Non-Parametric Tests: Alternatives To Parametric Approaches In Analyzing Skewed Or Outlier-Heavy Data

Which option should you use if your data is skewed or has outliers?

Median and IQR

If your data is skewed or has outliers, you can consider using a non-parametric approach rather than a parametric approach. A non-parametric approach does not make any assumptions about the underlying distribution of the data and is thus more robust to deviations from normality or the presence of outliers.

Some commonly used non-parametric tests include the Wilcoxon rank-sum test, the Kruskal-Wallis test, and the Mann-Whitney U test. These tests are alternatives to the parametric t-test, ANOVA, and other tests that assume normality or homogeneity of variance.

It’s important to note that non-parametric tests may have less power than their parametric counterparts in situations where the assumptions of the parametric tests are met. Therefore, it’s essential to carefully examine your data and choose an appropriate test based on your research question, the type of data you have, and the specific assumptions associated with each test.

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