This enduring "chestnut" of an issue warrants periodic refreshing. An on-going discussion is found here. Those interested in the long-standing “classic” treatment of such issues, Edward Tufte’s, The Visual Display of Quantitative Information (2nd ed., 2001) delves into the “theory and practice in the design of data graphics,” and includes illustrated examples of the “best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis.” Finally, in a 2011 essay Andrew Gelman (Columbia--Statistics) wades into these matters with a normative (and intentionally provocative) take favoring "tables over graphs." The Gelman paper's abstract follows.
"The statistical community is divided when it comes to graphical methods and models. Graphics researchers tend to disparage models and to focus on direct representations of data, mediated perhaps by research on perceptions but certainly not by probability distributions. From the other side, modelers tend to think of graphics as a cute toy for exploring raw data but not much help when it comes to the serious business of modeling. In order to better understand the benefits and limitations of graphs in statistical analysis, this article presents a series of criticisms of graphical methods in the voice of a hypothetical old-school analytical statistician or social scientist. We hope to elicit elaborations and extensions of these and other arguments on the limitations of graphics, along with responses from graphical researchers who might have different perceptions of these issues."
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