ABSTRACT
In this chapter, the authors encounter the one-way analysis of variance (Analysis of variance (ANOVA), for short) for comparing more than two group means. They also explore how it is related to the t-test, and why they can't just use multiple t-tests to do multiple group comparisons. Omnibus tests are simply overall tests that evaluate the entire set of data all at once. In the case of the ANOVA, this will mean evaluating how different all of the groups are, taken together. As with the independent samples t-test, omega squared is interpreted as the proportion of variance explained by the grouping variable. As a final note on selecting the appropriate post-hoc test, it is never acceptable to “try out” various post-hoc tests in the same sample to see which one gives the desired result. Planned contrasts or a priori comparisons allow us to specify how we think the groups will differ beforehand.
