Data transformations have been promoted as a popular and easy-to-implement remedy to address the assumption of normally distributed errors (in the population) in linear regression. However, the application of data transformations introduces non-ignorable complexities which should be fully appreciated before their implementation. This paper adds to existing "Practical Research and Assessment Evaluation" ("PARE") publications on data transformations by providing a broad overview underlying the use of data transformations for the specific purpose of statistical inference and interpreting meaningful effect sizes. Data transformations not only potentially change the scale of the transformed variable; they also alter the fundamental relationships among variables while simultaneously changing the distribution of the errors. Given these repercussions, we clarify the nature of certain data transformations and strongly recommend the use of data transformations when they can enhance the interpretation of effect sizes.