Which statistical method is most suitable for comparing more than two groups' means?

The most suitable statistical method for comparing the means of more than two groups is ANOVA, which stands for Analysis of Variance. ANOVA is specifically designed to assess whether there are statistically significant differences between the means of three or more independent groups. It does this by analyzing the variability within the groups compared to the variability between the groups.

When using ANOVA, you can efficiently determine if at least one group mean is different from the others without conducting multiple t-tests, which increases the risk of Type I errors (false positives). The result of ANOVA indicates if the differences observed are larger than would be expected by chance alone, thereby providing a solid basis for inferring that not all group means are equal.

In contrast, t-tests are limited to comparing the means of only two groups at a time, making them unsuitable for scenarios involving more than two groups. The chi-square test is primarily used for categorical data to assess how observed frequencies compare to expected frequencies, not for comparing means. Regression analysis, while useful for examining relationships between variables, is not designed specifically for comparing group means.

Thus, ANOVA is the preferred method for analyzing mean differences across multiple groups due to its robustness and its statistical design tailored for this purpose.