(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
The visual display of quantitative information (to use Edward Tufte’s wonderful term) is a diverse field or set of fields, and its practitioners have different goals. The goals of software designers, applied statisticians, biologists, graphic designers, and journalists (to list just a few of the important creators of data graphics) often overlap—but not completely. One of our aims in writing our article [on Infovis and Statistical Graphics] was to emphasize the diversity of graphical goals, as it seems to us that even experts tend to consider one aspect of a graph and not others.
Our main practical suggestion was that, in the internet age, we should not have to choose between attractive graphs and informational graphs: it should be possible to display both, via interactive displays. But to follow this suggestion, one must first accept that not every beautiful graph is informative, and not every informative graph is beautiful. . . .
Yes, it can sometimes be possible for a graph to be both beautiful and informative, as in Minard’s famous Napoleon-in-Russia map, or more recently the Baby Name Wizard, which we featured in our article. But such synergy is not always possible, and we believe that an approach to data graphics that focuses on celebrating such wonderful examples can mislead people by obscuring the tradeoffs between the goals of visual appeal to outsiders and statistical communication to experts. . . .
The full article (with Antony Unwin) is here. We are responding to discussions by Robert Kosara, Stephen Few, Hadley Wickham, and Paul Murrell.
I’m hoping that, by framing graphics in terms of tradeoffs, we can move the discussion forward. In our earlier discussions of statistical graphics and data visualization, we were slammed by statisticians for being too nice to infovis, and slammed by infovis people for being too mean. Here we’re emphasizing that:
1. You can’t expect to satisfy all goals with a single display, and thus,
2. Multiple graphs of a single page, or on multiple pages, are typically the way to go.
Please comment on the article here: Statistical Modeling, Causal Inference, and Social Science