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Analysis of variance (ANOVA) is a statistical test for detecting differences in group means when there is one parametric dependent variable and one or more independent variables. This article summarizes the fundamentals of ANOVA for an intended benefit of the clinician reader of scientific literature who does not possess expertise in statistics. The emphasis is on conceptually-based perspectives regarding the use and interpretation of ANOVA, with minimal coverage of the mathematical foundations. Computational examples are provided. Assumptions underlying ANOVA include parametric data measures, normally distributed data, similar group variances, and independence of subjects. However, normality and variance assumptions can often be violated with impunity if sample sizes are sufficiently large and there are equal numbers of subjects in each group. A statistically significant ANOVA is typically followed up with a multiple comparison procedure to identify which group means differ from each other. The article concludes with a discussion of effect size and the important distinction between statistical significance and clinical significance.
Steven F. Sawyer (Wed,) studied this question.