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Proofiness: The Dark Arts of Mathematical Deception ReviewOne of the benefits of retiring from my career as a statistician is that I no longer feel it's my personal responsibility to alert friends and colleagues to the myriad ways they are being misled or deceived by the kind of abominably poor summarization of data that's pretty much the norm these days. It's just as well - who wants to be *that guy*, the crank at the table who people start to inch away from surreptitiously, avoiding eye contact all the while?
Not that I endorse misleading or deceptive data presentation - far from it. Now more than ever, as we all struggle to make sense of the avalanche of information that constantly assails us, the capacity for critical, intelligent interpretation is vital. So it's important to be able to see through the most prevalent fallacies in data interpretation, not to mention data presentation strategies deliberately intended to mislead. This latest book by Charles Seife has the laudable goal of educating the reader about some of the most common types of statistical malpractice out there, continuing a tradition established by such authors as Darrell Huff ("How to Lie With Statistics"), John Paulos ("Innumeracy"), Edward Tufte, or the authors of last year's highly successful "The Numbers Game" (Michael Blastland and Andrew Dilnot).
Unfortunately, though "Proofiness" is a well-intentioned book, it suffers from a fundamental crisis of identity. There is a major gap between what "Proofiness" promises and what Seife actually delivers. The first hundred pages cover roughly what one might expect: graphical deception by use of misleading labels or scales, comparison of apples and oranges (e.g. dollar amounts unadjusted for inflation, absence of an appropriate control group, regression to the mean), cherry-picking of data, the tendency to interpret mere random variation as systematic, nonsensical conclusions obtained by extrapolating beyond the range of observed data, overstatement of the precision of measurements, the way in which humans are hard-wired to misinterpret risk and deal poorly with calculations involving risk. Seife's exposition of these topics is lively and clear (with the major caveat discussed below). About halfway through the chapter on risk, however, he makes a major detour. His discussion of the malfeasance of those involved in the Enron debacle, the Bernie Madoff pyramid scheme, the failures at AIG, Citigroup and other institutions, and the subsequent bailout efforts has almost nothing to do with statistical trickery, focusing instead on the public policy and regulatory issues raised by the financial meltdown.
The next chapter, "Poll Cats" does return to the issues involved in conducting accurate sample surveys and presenting the data appropriately, with a reasonably clear discussion of systematic error versus random error. However, the following two chapters, "Electile Dysfunction" and "An Unfair Vote", taking up some 80 pages, really have little to do with data-related issues. Instead they provide a review of events surrounding the Florida vote count in the 2000 presidential election, the six-month circus that took place before Al Franken was eventually declared winner in the 2008 Minnesota Senate race, and a review of historical and present-day gerrymandering efforts whenever congressional redistricting comes up for discussion. Not that Seifen's review of the relevant events, and the issues they raise, is not interesting - but it is largely editorial comment on political events and, as such, it seems to belong in a different book, as does the appendix in which he discusses electronic voting. In making this criticism, I take the view that fraud, malfeasance and corruption stemming from poor public policy, faulty regulatory mechanisms, or inadequate enforcement of existing protections, really are subjects for a different kind of book than that initially described by Seifen. Though the author does return to his initial remit in the final two chapters (discussing abuse of probability and statistical arguments within the judicial system, and for propaganda purposes), overall the book does not make a coherent whole.
Then there's the caveat mentioned above, regarding Seife's exposition methods, which turns out to be a serious one, enough to prevent me from giving this book my endorsement, despite its good intentions. It's the author's predilection for coining cutesy neologisms that not only add nothing to the discussion, but actually end up seriously muddying the exposition. It's evident right there in the book's faux-cute title, "Proofiness". I wish I could say that the author offers a rigorous definition of exactly what he means by this invented term, but he doesn't. It remains unhelpfully vague throughout the book. Sadly, it's not the only example of authorial neologism run amok. "Disestimation", "Potemkin numbers", "randumbness", "regression to the moon", and the horrendous coinage "causuistry"; each of these is a neologism that adds nothing to the discussion. Many of them lack a clear definition, or when a definition is offered, the term just seems to muddy the waters. For instance, Seife uses "disestimation" to mean "overstatement of the precision of a number or measurement", indicating an error based in randomness. But the 'dis'-prefix clearly suggests a systematic error, as does the parallelism with "misestimation", a term which statisticians routinely use to indicate a systematic error. And while one applauds the author's efforts to educate his readership about the error of mistaking correlation for causation, the term "causuistry" is simply an abomination. I'm not sure where this recent trend for authors to invent their own faux-cutesy terminology, where none is needed, originates (possibly Malcolm Gladwell bears some of the responsibility), but it needs to stop.
Though I am sympathetic to the author's stated aims, his execution was such that I cannot endorse this book. Anyone interested in this important topic would be far better served by reading The Numbers Game: The Commonsense Guide to Understanding Numbers in the News, in Politics, and inLife by Michael Blastland and Andrew Dilnot.
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