Which statistical test would you use to assess whether there is a significant difference in means between more than two groups?

The appropriate statistical test to assess whether there is a significant difference in means between more than two groups is ANOVA, which stands for Analysis of Variance. ANOVA is specifically designed to compare the means from three or more independent groups to determine if at least one group mean is statistically different from the others.

The rationale behind using ANOVA stems from its ability to analyze the impact of one or more independent categorical variables on a continuous dependent variable. When comparing more than two means, performing multiple t-tests increases the risk of Type I errors, where you might incorrectly reject a true null hypothesis. ANOVA addresses this issue by testing the null hypothesis that all group means are equal simultaneously, thus providing a more robust statistical analysis.

Using a chi-square test is appropriate when dealing with categorical data to assess relationships between variables, while a t-test is limited to comparing the means of two groups. The Mann-Whitney U test is a non-parametric alternative to the t-test for comparing two independent groups, making it unsuitable for more than two groups.