Which nonparametric test is suitable for data collected from two independent samples with non-normally distributed scores?

The Mann-Whitney U Test is particularly suitable for comparing the scores of two independent samples, especially when the scores do not follow a normal distribution. This nonparametric test assesses whether there is a statistically significant difference between the distributions of the two samples without relying on the assumption of normality, making it an ideal choice for data that does not meet the criteria for parametric tests like the t-test.

By ranking all the data points from both groups together and then comparing the ranks between the two independent samples, the Mann-Whitney U Test can effectively determine if one sample tends to have higher or lower values than the other. This method of analysis is robust against outliers and works well with ordinal data or continuous data that lacks a normal distribution, further solidifying its utility in social sciences and other research fields that frequently encounter non-normally distributed data.

In contrast, the Wilcoxon signed-rank test is meant for paired or matched samples rather than independent samples, which limits its applicability here. The Kruskal-Wallis test is used when comparing more than two groups, and the Chi-square test is suitable for categorical data rather than for continuous scores. Thus, the Mann-Whitney U Test stands out as the correct choice for