Used to compare results of more than two groups

• A. Analysis of Variance (ANOVA)
• B. T-test
• C. Chi Square
• D. Repeated measures ANOVA

Answer: (A)

Comparing results across more than two groups requires a method that tests for any difference among means while controlling the overall error rate. Analysis of Variance (ANOVA) does this by partitioning total variability into between-group and within-group components and using the F statistic to determine if the between-group difference is larger than would be expected by chance. If the test is significant, it indicates that at least one group mean differs, and post hoc tests can identify which groups differ. The t-test is limited to two groups at a time, and performing multiple t-tests without proper adjustment inflates the chance of false positives. The chi-square test is for categorical data, assessing relationships or distributions rather than comparing numeric means. Repeated measures ANOVA handles related samples—same subjects measured under different conditions—so it accounts for correlations within subjects rather than comparing independent groups. For comparing more than two groups on a numeric outcome, ANOVA is the appropriate choice.