Introduction
Parametric analysis of variance assumes the data are from normally distributed populations with the same variance and there is independence, both within and among treatments. If these assumptions are not met, an ANOVA may give you an unrealistic F statistic and therefore an unrealistic probability that several sample means are from the same population. Therefore, it is important to know how robust ANOVA is to violations of these assumptions and what to do if they are not met as in some cases it may be possible to transform the data to make variances more homogeneous or give distributions that are better approximations to the normal curve.
This chapter discusses the assumptions of ANOVA, followed by three frequently used transformations. Finally, there are descriptions of two tests for the homogeneity of variances.
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