# “Data is Personal” and the maturing of the literature on statistical graphics

1. Exhortations to look at your data, make graphs, do visualizations and not just blindly follow statistical procedures.

2. Criticisms and suggested improvements for graphs, both general (pie-charts! double y-axes! colors! labels!) and specific.

3. Instruction and examples of how to make effective graphs using available software.

4. Demonstration or celebration of particular beautiful and informative graphs.

5. Theorizing about what makes certain graphs work well or poorly.

We’ve done lots of all these over the years—this blog has about 600 posts on statistical graphics—as have Kaiser Fung and others, following the lead of Tukey, Cleveland, Tufte, Wainer, etc. When writing about graphics, the above five things are what we do.

Almost always when we and others write about statistical graphics, it is in the spirit of exhortation, criticism, celebration, demonstration, or instruction—but not of open inquiry.

Yes, my views on graphics have evolved, I’m open to new ideas, and in some of my writings I’ve thought hard about the virtues of other perspectives (as in this paper with Antony Unwin on different goals in visualization)—but just about always we’re writing to advance some argument or to simply celebrate the virtues of graphical display.

There’s been some literature on comparative evaluation of different graphical approaches, but much of what I’ve seen in that area hasn’t been so impressive. It’s hard to quantitatively evaluate something as slippery as statistical graphics, given the many goals that graphs serve.

With that as background, I was very happy to read this post, “Data is Personal. What We Learned from 42 Interviews in Rural America,” where Evan Peck describes a study he did with Sofia Ayuso and Omar El-Etr:

We asked 40+ people from rural Pennsylvania to rank a set of 10 graphs. Then we talked about it.

At a farmers market, a construction site, and in university dining facilities, we interviewed 42 members of our community about graphs and charts to understand how they understand and engage with data.

We showed people 10 data visualizations about drug use that varied in their visual encodings, their style, and their source.

We asked them to rank the 10 graphs (without source information!) based on their usefulness.

After revealing the sources of the graphs, people were given an opportunity to rerank their visualizations.

The people we talked to weren’t just young and weren’t just in college. They diverse in their education (60% never completed college) and age (26% were 55+, 33% were between 35–44). Through many hours of conversations, here is what we found . . .