Our approach is to encourage readers to understand the models underlying the most common experimental designs. We describe the models that are appropriate for various kinds of biological data – continuous and categorical response variables, continuous and categorical predictor or independent variables. Our emphasis is on general linear models, and we begin with the simplest situations – single, continuous variables – describing those models in detail. We use these models as building blocks to understanding a wide range of other kinds of data – all of the common statistical analyses, rather then being distinctly different kinds of analyses, are variations on a common theme of statistical modeling – constructing a model for the data and then determining whether observed data fit this particular model. Our aim is to show how a broad understanding of the models allows us to deal with a wide range of more complex situations. We have illustrated this approach of fitting models primarily with parametric statistics. Most biological data is still analyzed with linear models that assume underlying normal distributions. However, we introduce readers to a range of more general approaches, and stress that, once you understand the general modeling approach for normally-distributed data, you can use that information to begin modeling data with nonlinear relationships, variables that follow other statistical distributions, etc.

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