- In statistics, the ANOVA test is an ANalysis Of VAriance (wikitionary). Within the context of gene expression the one-way analysis of variance (ANOVA) tests allow you to determine if one given factor, such as drug treatment, has a significant effect on gene expression behavior across any of the groups under study. A significant p-value resulting from a 1-way ANOVA test would indicate that a gene is differentially expressed in at least one of the groups analysed. ANOVA is useful in comparing two, three or more groups avoiding the error inherent in performing multiple t-tests. If we have 3 treatments to compare (A, B, C) then we would need 3 separate t-tests (comparing A with B, A with C, and B with C). If we had seven treatments we would need 21 separate t-tests. This would be time-consuming but, more important, it would be inherently flawed because in each t-test we accept a 5% chance of our conclusion being wrong (when we test for p = 0.05). So, in 21 tests we would expect (by probability) that one test would give us a false result. ANOVA overcomes this problem by enabling us to detect significant differences between the treatments as a whole.