A ‘non-parametric test’ used to compare three or more independent groups of sampled data?
**Core Concept**
A non-parametric test is a statistical method used to analyze data that does not follow a normal distribution or when the data is ordinal or categorical. In the context of comparing multiple groups, non-parametric tests are particularly useful as they do not require assumptions about the distribution of the data.
**Why the Correct Answer is Right**
The correct answer is the Kruskal-Wallis test, which is a non-parametric method used to compare three or more independent groups of sampled data. The Kruskal-Wallis test ranks the data from all groups and then calculates the sum of the ranks for each group. This sum of ranks is then compared across groups to determine if there are any significant differences. The Kruskal-Wallis test is a useful alternative to the one-way ANOVA when the data does not meet the assumptions of normality or equal variances.
**Why Each Wrong Option is Incorrect**
**Option A:** This option is incorrect because the Mann-Whitney U test is used to compare two independent groups of sampled data, not three or more.
**Option B:** This option is incorrect because the Friedman test is used to compare three or more related groups of sampled data, not independent groups.
**Option D:** This option is incorrect because the Wilcoxon signed-rank test is used to compare two related groups of sampled data, not three or more independent groups.
**Clinical Pearl / High-Yield Fact**
When choosing a non-parametric test, remember to consider the distribution of the data and the type of comparison being made. Non-parametric tests are useful when the data is ordinal or categorical, or when the data does not meet the assumptions of parametric tests.
**Correct Answer: C. Kruskal-Wallis test**