Which statistical test is most appropriate for comparing changes in low-density lipoprotein cholesterol (LDL) across three groups in a clinical trial?

When comparing changes in low-density lipoprotein cholesterol (LDL) across three groups in a clinical trial, analysis of variance (ANOVA) is the most appropriate statistical test. ANOVA is designed to assess whether there are statistically significant differences in the means of three or more independent groups. In this context, you are specifically interested in the changes in LDL levels across different groups, which can reflect the impact of various treatments or interventions.

Using ANOVA allows the researcher to evaluate the overall effect of the treatment on LDL levels in a single analysis, rather than performing multiple t-tests, which could increase the risk of Type I error. If significant differences are detected, post-hoc tests can further identify where these differences lie among the groups. This makes ANOVA particularly efficient and suitable for the scenario described.

The other statistical tests listed pertain to different types of data or specific situations. The chi-square test is used for categorical data to assess the association between two categorical variables and is not applicable for continuous data like LDL levels. The Mann-Whitney U test is a non-parametric test used to compare two independent groups, which does not fit the requirement of comparing three groups. The t-test is appropriate for comparing the means of two groups rather than three