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

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

• A. Independent T test
• B. ANOVA
• C. Mann-Whitney U test
• D. Linear regression

Answer: (C)

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.

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.