Which test is favored when working with skewed data?

When working with skewed data, the Mann Whitney U test is favored because it is a non-parametric test. Non-parametric tests do not assume a specific distribution for the data, making them suitable for datasets that do not meet the normality assumption required by parametric tests, such as the t-test or ANOVA.

The Mann Whitney U test is particularly useful because it assesses whether the distributions of two independent groups differ, regardless of the shape of the distribution. It operates on the ranks of the data rather than the raw data values, which reduces the impact of skewness on the results.

In contrast, the Student t-test, paired t-test, and one-way ANOVA all rely on the assumption that the data is normally distributed, which can lead to inaccurate results when applied to skewed data. These tests measure differences in means rather than distributions, making them inappropriate for datasets that deviate significantly from normality. Thus, in the context of skewed data, utilizing the Mann Whitney U test is the most appropriate choice.