Thursday, August 16, 2018

Tufte: Beautiful Evidence

Last year I read a bunch of Edward Tufte books: The Visual Display of Quantitative Information, Envisioning Information, Visual Explanations: Images and Quantities, Evidence and Narrative, Beautiful Evidence, and The Cognitive Style of PowerPoint. I found them compelling and ended up writing You've Got To See This for the Gurock Blog. 

In the intervening year I've found ways to incorporate aspects of what I learned into my work: I've tried hard to remove the junk from my figures and charts; I've noted that when we're talking about how to talk about our data, something like small multiples can help us to visualise more of it more easily; I've encouraged members of my team to think about the difference between exploring data in a tool such as Excel, and presenting data in a chart produced by Excel.

After that experience, I thought it might be interesting to review the notes I took as I went through the books (which I did, and it was). Then I thought it might also be useful to share them (which I'm doing, and you can judge).

This short set of posts contain the quotes I took from each book, presented in the order that I happened to read them. Themes recur across the series, but the quotes don't necessarily reflect that; instead they show something of what I felt was interesting to me in the context of what I'd already read, what I already knew, and what I was working on at the time.

All of the books are published by Graphics Press and available direct from the author at Particular thanks go to Šime for the loans and the comments.


For showing evidence, the map metaphor suggests that labels belong on images, that external grids help to scale images, and that data are more credible when contextualised. (p. 21)

The idea is to be approximately right rather than exactly wrong. (p. 50)  [so show more data at a lower precision to give context etc, c.f. sparklines]

[by adding sparklines to tables] Readers can scan ... making simultaneous multiple comparisons, searching for nonrandom patterns (p. 51)

Variations in slopes are best detected when the slopes are around 45 degrees ... aspect ratios should be such that time-series graphics tend towards a lumpy profile (p. 60)

A good way to assess a display for unintentional optical clutter is to ask "Do the prominent visual effects convey relevant content?" (p. 62)

[When creating diagrams with links] focus on causality (p. 78)

The essential test of text/image relations is how well they assist understanding of the content (p. 88)

Show comparisons, contrasts, differences. (p. 127)

Show multivariate data; that is, show more than one or two variables (p. 130)

... explanatory investigations, if they are to be honest and genuine, must seek out and present all relevant evidence regardless of mode (p. 131)  [where "mode" means the kind of data, e.g. text, image, table etc]

The first question is What are the content-reasoning tasks that this display is supposed to help with? (p. 136)

If the intellectual task is to make comparisons, as it is in nearly all data analysis then "Show comparisons" is the design principle. (p. 137)

Like the passive voice, the bullet-list format collaborates with evasive presenters to promote effects without causes (p. 143) [on the design choices that PowerPoint imposes]

proxy [is] statistical jargon for "pun" (p. 149) [and so do not use the same word to refer to multiple things in the same analysis, nor confound the things that one concept represents]

PowerPoint is presenter-oriented, not content-oriented, not audience-oriented (p. 158)
Image: Edward Tufte

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