When evaluating differences among more than two groups, which test should be used?

The appropriate statistical test for evaluating differences among more than two groups is known as ANOVA (Analysis of Variance). ANOVA is specifically designed to compare means across multiple groups simultaneously, allowing researchers to determine if at least one group mean is significantly different from the others.

When researchers are interested in comparing only two groups, a t test is suitable for statistical analysis. However, as soon as there are three or more groups involved, using multiple t tests increases the risk of Type I error, which is the likelihood of incorrectly rejecting a null hypothesis when it is, in fact, true. Thus, ANOVA avoids this issue by incorporating all group variances into a single comprehensive test.

T tests come in various forms such as independent samples t tests and paired-samples t tests, which are focused on comparing two means. The inclusion of the t test as an option alongside ANOVA, in the context of comparing multiples groups, is misleading because when the focus is on multiple groups, ANOVA is the correct and preferred approach due to its ability to handle comparisons efficiently and without inflated error rates.