American Board of Surgery Qualifying Exam (ABS QE) Practice Test 2025 - Free Surgery Exam Practice Questions and Study Guide

The use of ANOVA (Analysis of Variance) is appropriate for analyzing normal, continuous data when comparing means from more than two groups. ANOVA allows researchers to determine if there are statistically significant differences among the group means, making it an essential tool in experimental and observational studies where multiple groups are compared.

The fundamental concept behind ANOVA is that it assesses the variance within each group and the variance between the groups. If the variance between the groups is significantly greater than the variance within the groups, it suggests that at least one group mean is different from the others. This approach works under the assumption that the data is normally distributed and that the groups have equal variances (homogeneity of variance).

Other options are more specialized or suited for different types of data. For instance, the Mann-Whitney U-test is designed for comparing two independent groups and is used specifically for non-parametric data. The Chi-square test assesses categorical data to see if there is an association between two or more groups but does not handle continuous data. Friedman's ANOVA is a non-parametric alternative to one-way ANOVA and is utilized for related (or matched) groups rather than independent groups. Thus, when dealing with normal, continuous data across more than two groups,