A multiple comparison test, or MCT is a type of post hoc analysis generally conducted after comparing multiple samples using hypothesis tests such as ANOVA. When many groups are compared, or multiple factors are tested in some groups, the MCT mainly helps identify a specific group that is significantly different from the others, or a factor that causes a significant effect. For example, when comparing two groups of zebrafish it is easy to identify a group with a significantly different mean length at a 0.05 significance level. If we increase the number of test groups, it becomes increasingly difficult to find the group with significantly different mean. In such cases, a pairwise comparison also gives higher rates of Type-I error. MCT helps determine a significantly different group in such cases by correcting the alpha values to reduce the Type-I error. There are different types of MCTs that can be used for equal or unequal sample sizes. The most commonly used MCT is the Bonferroni test.