Much of the information that informed this week’s critical thinking podcast’s discussion of mathematics was drawn from Charles Seife’s 2010 book Proofiness: The Dark Arts of Mathematical Deception.
The title is clearly a play on the notion of “Truthiness,” a term coined by TV comic Stephen Colbert to describe the type of information you “know” to be true, even if you have no evidence to support it (since it confirms your own confirmation biases).
Proofiness focuses on a specific kind of information that plays this sort of “I know in my gut it’s true” role: quantitative information that is endlessly manipulated in the ways I discussed in the podcast (cherry picking, comparing apples to oranges, etc.) not necessarily to deceive opponents but to assure supporters that what they already believe is supported by the numbers.
Seife, a journalist who has written on a number of scientific topics (including a book on the history of the number zero that I must pick up), has a knack for coining phrases that sum up what’s wrong with our approach to numerical data. In addition to “proofiness,” you’ve got “fruit packing” that covers a range of manipulation techniques that, for whatever reason, are associated with produce (such as cherry picking, comparing apples to oranges, and “apple polishing” – cleaning up your numbers so that the option you prefer sounds as certain as possible).
Then you’ve got “randumbness” (our desire to see a pattern in what are really random events, such as the Gambler’s Fallacy that a losing streak means you are statistically due for a win), “regression to the moon” (following a trend to ludicrous extremes, such as projecting a company profit curve into the next century) and “dissestimation” (taking a number too literally, such as our religious belief that 98.6 degrees Farenheit represents “normal” body temperature).
With so many examples of proofiness around, I can’t fault Seife for picking the ones he feels to be the most egregious. Although I’m not sure if some of the examples he chooses (such as the gerrymandering of voting districts) represent “proofiness” (i.e., mathematical deception) vs. a non-numerical political phenomenon that has accelerated due to the ease with which data (like that covering voting district population ) can be crunched using modern computer hardware and software.
And while the author does include an appendix in which he explains the math behind the statistical error you find in polls and surveys, I wish he had spent a little more time covering the actual math which would have made some of his arguments seem less abstract.
But even if other volumes on this subject are likely to be written, Proofiness lays out a challenge that all of us should be thinking about. For if numbers can just as powerfully deceive as inform, it’s up to us to know which of these they are doing at any particular time.
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