What type of statistical test is most appropriate when comparing three or more group means?

The most appropriate statistical test for comparing three or more group means is ANOVA, which stands for Analysis of Variance. ANOVA is specifically designed to assess whether there are statistically significant differences among the means of different groups. It allows researchers to evaluate the impact of one or more independent variables on a dependent variable while taking into account the variability within and between groups.

In situations where only two group means are involved, other tests like the independent t-test or paired t-test are more appropriate. The independent t-test compares the means of two unrelated groups, while the paired t-test assesses the means of two related groups, often used in before-and-after scenarios on the same subjects.

When dealing with three or more groups, using multiple t-tests would increase the risk of Type I error, which is why ANOVA is preferred. It controls for this error across multiple comparisons.

Nonparametric tests, while useful, are not the primary method for comparing means when the assumptions of ANOVA are met. These tests are typically employed when the data do not meet the assumptions required for parametric tests like ANOVA, such as normality or homogeneity of variance. Therefore, in the context of the given question, ANOVA is the best choice for comparing the means across