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

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What test would you choose for analyzing non-parametric, paired nominal data?

Chi-squared
Wilcoxon rank sum
Mann-Whitney
McNemar

The correct answer is: McNemar

When analyzing non-parametric, paired nominal data, the McNemar test is the appropriate choice. This test specifically evaluates changes in responses from the same subjects across two related conditions or time points, making it ideal for paired nominal data. For instance, in a study measuring the effectiveness of a treatment by comparing binary outcomes (like "success" or "failure") before and after the treatment in the same group of individuals, the McNemar test can determine if there is a significant difference in the proportions of outcomes. In contrast, other tests mentioned do not quite fit paired nominal data. The Chi-squared test is used for analyzing categorical data to assess how likely it is that an observed distribution is due to chance, but it requires independent samples. The Wilcoxon rank sum and Mann-Whitney tests are non-parametric tests designed for continuous or ordinal data to compare two independent samples, which does not apply in the case of paired nominal data. Thus, the McNemar test stands out as the appropriate method for this type of analysis, allowing for the assessment of changes in paired observations.