Critical Inquiry Exam 2 Practice

When you have more than two groups, the goal is to test whether the group means differ without inflating the chance of a false positive. Analysis of Variance does this by comparing variability between the group means to variability within each group. If all group means are equal, the between-group variation is small and the F statistic is near 1, so you don’t reject the null. If at least one group mean differs, the between-group variation increases, making the F statistic larger and more likely to indicate a difference among means. This approach tests all group means in one go, avoiding the multiple-comparisons problem that comes with performing several t-tests. If the ANOVA result is significant, you can follow with post hoc tests to identify which specific groups differ, with appropriate adjustments. Repeated measures ANOVA is used when the same subjects are measured under multiple conditions, not for independent groups. Chi-square is for associations between categorical variables, and a t-test is only for comparing two means. So, for comparing means across more than two groups, ANOVA is the typical choice.