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Performing inferential tests to help draw conclusions about population parameters is useful, but ultimately the findings of a statistical analysis should make their way into a graph or other visualization. Data visualization is a field in itself, and with the advent of modern computing power, possibilities exist today that could only be dreamt of in the past. Simple visualizations such a histograms, boxplots, scatterplots, etc., can be useful in depicting findings but also in helping to verify assumptions that underlay the statistical model one is using. For example, since many tests of normality and equality of variances (and covariances) are relatively sensitive to the types of data to which they are applied, oftentimes researchers will generate simple plots in order to detect potential gross violations of such assumptions. We feature such techniques throughout the book.

For graphical displays meant to communicate findings (rather than test assumptions), Friendly (2000) puts the field into context:

Designing good graphics is surely an art, but as surely, it is one that ought to be informed by science … In this view, an effective graphical display, like good writing, requires an understanding of its purpose – what aspects of the data are to be communicated to the viewer. In writing, we communicate most effectively when we know our audience and tailor the message appropriately. (p. 8)

In high‐dimensional space, the challenge of graphical approaches is to summarize data into lower dimensions, while still retaining most of the information in the original data. We feature some such plots in later chapters. For a thorough account of data visualization, see datavis.ca (Friendly, 2020). For sophisticated graphics using R, consult Wickham (2009).

For now, it is useful to briefly review some basic plots for which the reader is likely already familiar.