Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.

It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number of samples increases. This is because the number of sample pairs to be compared or pairwise comparisons increases with the number of samples. Further, the percentage of Type-I error increases with the number of pairwise comparisons.

An MCT will help identify the significantly different mean among multiple samples by correcting the significance alpha values and reducing the Type-I error. Additionally, one can use different MCTs for datasets with equal or unequal sample sizes. An example of a commonly used MCT is the Bonferroni test.