Analysis of Variance

The term analysis of variance (ANOVA) describes a group of inferential statistical tests that were developed by the British statistician Sir Ronald Fisher. Whereas a t-test is used in statistics to determine if the means of two groups differ significantly, analysis of variance allows for comparison of more than two groups. ANOVA evaluates the null hypothesis that in a set of k independent samples (where k ≥ 2), all k samples are drawn from the same population, with the alternate hypothesis that at least two of the samples are drawn from populations with different mean values. The test statistic computed is based on the F distribution. In the case of two samples (i.e., a comparison of 2 means), the t-test and the F-test are equivalent (to be exact, F = t2); therefore, ANOVA is most frequently used when k > 2.

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Links

• Single Factor ANOVA: Jerry Dallal (Tufts University)
• How to Read the Output From One-Way Analysis of Variance: Jerry Dallal (Tufts University)
• Multiple Comparisons: Jerry Dallal (Tufts University)
• Labeling Similar Means After Performing an Analysis of Variance: Jerry Dallal (Tufts University)
• Analysis of Variance Topics: UCLA Stat Computing Site