We start by focusing on the important assumption of additivity for the effects of predictors in ANOVA models. Using a practical example, we show how the predictors used to explain response variables in biology oftentimes operate on a multiplicative scale, and thus stress the need to take this into account when choosing an appropriate data transformation. We introduce three important monotonic transformations, namely the log transformation, arcsine transformation, and square-root transformation. We thoroughly discuss the advantages and possible dangers of individual transformation types. The methods described in this chapter are accompanied by a carefully-explained guide to the R code needed for their use.
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