What is the purpose of ANOVA?

• A. Comparing 2 or more levels of an independent variable (parametric)
• B. Comparing two independent samples' variances
• C. Measuring correlation between variables
• D. Testing normality of distribution

Answer: (A)

ANOVA is used to determine whether the means across two or more groups defined by a categorical independent variable differ in a way that isn’t likely due to random variation. It works by comparing how much of the total variation in the data is due to differences between the group means versus how much is due to variability within each group, and it produces an F statistic to test this. If the F test is significant, it suggests that at least one group mean is different from the others, though it doesn’t specify which groups differ—that’s where post-hoc tests come in. This is a parametric method, relying on assumptions about normality, equal variances, and independent observations. When there are only two groups, ANOVA essentially behaves like a two-sample t-test, but with more groups, it controls the Type I error rate better than performing multiple t-tests. In short, its main purpose is to compare means across two or more levels of a factor to see if they differ meaningfully.