American Board of Surgery Qualifying Exam (ABS QE) Practice Test

Which statistical test is appropriate for analyzing non-parametric, unpaired ordinal data?

1. Wilcoxon rank sum

2. McNemar

3. Mann-Whitney

4. Chi-squared

The correct answer is: Mann-Whitney

The Mann-Whitney test is the appropriate choice for analyzing non-parametric, unpaired ordinal data. This test, also known as the Wilcoxon rank-sum test, is designed specifically to compare differences between two independent groups when the data cannot be assumed to be normally distributed. It ranks all the data points from both groups together and then compares the sum of the ranks between the two groups. The use of ranks instead of raw data makes it particularly suited for ordinal data, which does not meet the assumptions necessary for parametric tests, such as the t-test. While the Wilcoxon rank sum test is indeed another name for the Mann-Whitney test, the terminology and context may differ among various texts or clinical applications. However, this highlights the test's relevance to the scenario presented. Other options may not be the best fit for this type of data. For instance, the Chi-squared test is typically used for categorical data rather than ordinal data, while McNemar's test is suited for paired nominal data. Therefore, the Mann-Whitney test remains the optimal choice for comparing unpaired ordinal data in a non-parametric setting.