Thursday, August 16, 2018

Tufte: Visual Explanations


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 edwardtufte.com. Particular thanks go to ┼áime for the loans and the comments.

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How are we to assess the integrity of visual evidence? What ethical standards are to be observed in the production of such images? (p. 25)

... the reason we seek causal explanations is in order to intervene, to govern the cause so as to govern the effect ... (p. 28)

... descriptive narration is not causal explanation; the passage of time [can be] a poor explanatory variable ... (p. 29)

The deep, fundamental question in statistical analysis is Compared with what? (p. 30)

Time-series are exquisitely sensitive to choice of intervals and end points. (p. 37)

Displays of evidence implicitly but powerfully define the scope of the relevant, as presented data are selected from a larger pool of material. Like magicians, chartmakers reveal what they choose to reveal. (p. 43)

When assessing evidence, it is helpful to see a full data matrix, all observations for all variables, those private numbers from which the public displays are constructed. Not telling what will turn up. (p. 45)

... there are right ways and wrong ways to show data: there are displays that reveal the truth and displays that do not. (p. 45)

... lack of visual clarity in arranging evidence is a sign of a lack of intellectual clarity in reading about evidence (p. 48)

Informational displays should serve the analytical purpose at hand: if the substantive matter is a possible cause-effect relationship, then graphs should organize data so as to illuminate such a link. (p. 49)

In magical performances, knowledge about the revealed frontview (what appears to be done) fails to yield reliable knowledge about the concealed backview (what is actually done) — and it is the audience's misdirected assumption about such symmetric reliability that makes the magic. (p. 55)

[techniques of deception practised by magicians], when revered, reinforce strategies of presentation used by good teachers. Your audience should know beforehand what you are going to do; then they can evaluate how your verbal and visual evidence supports your argument. (p. 68)

If a clear statement of the problem cannot be formulated, then that is a sure sign that the content of the presentation is deficient. (p. 68)

Relevant to nearly every display of data, the smallest effective difference is the Occam's razor ... of information design. (p. 71)

Congruity of structure across multiple images gives the eye a context for assessing data variation. (p. 82)

Multiple images reveal repetition and change, pattern and surprise — the defining elements in the idea of information. (p. 105)

Excellence in the display of information is a lot like clear thinking. (p. 141)
Image: Tufte

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