Comparing More than Two Means

The problem of comparing more than two means results from the increase in Type I error that occurs when statistical tests are used repeatedly.

Completely randomized designs study the effects of one primary factor without the need to take other nuisance variables into account.

ANOVA is useful in the multiple comparisons of means due to its reduction in the Type I error rate.

Block design is the arranging of experimental units into groups (blocks) that are similar to one another, to control for certain factors.

A full factorial experiment is an experiment whose design consists of two or more factors with discrete possible levels.