Which of the following tests would be most appropriate for analyzing data that does not meet the assumptions of normality?

The Mann-Whitney U test is particularly suitable for analyzing data that does not meet the assumptions of normality because it is a non-parametric test. Non-parametric tests do not assume a specific distribution for the data, making them appropriate for situations where the underlying distribution of the sample cannot be determined to follow a normal distribution.

In contrast to the other options, the Independent T test, ANOVA, and Linear regression are all parametric tests. They rely on the assumption that the data follows a normal distribution and typically require homogeneity of variance among groups. When these assumptions are violated, the results from these tests may be unreliable, and therefore, using a non-parametric test like the Mann-Whitney U test becomes advantageous. This test ranks the data rather than relying on actual values, further enhancing its robustness in analyzing non-normally distributed data.