This essay (serialized here across 24 separate posts) uses words and numbers to discuss the uses of words and numbers — particularly examining evaluations of university degrees that employ statistical data to substantiate competing claims. Statistical analyses are crudely introduced as the mode du jour of popular logic, but any ratiocinative technique could likely be inserted in this re-fillable space and applied to create and defend categories of meaning with or without quantitative support. Questions posed across the series include: Is the data informing or affirming what we believe? What are the implications of granting this approach broader authority? The author, Melanie Williams, graduated from UA in 2006, with a B.A. in Anthropology and Religious Studies.
Nate Silver’s B.A. in Economics landed him his own crappy job, which led him to cultivate an interest in baseball statistics that preceded his fortuitous entrée into political forecasting. Silver’s early influences included the pioneers of sabermetrics, so I dug up an edition of the New Baseball Historical Abstract of liberal arts major Bill James. Maybe Bill can speak to his own motivations for collecting the baseball statistics he helps innovate:
“Baseball statistics are simplifications of much more complex realities. It may be unnecessary to say this because, of course, all human understanding is based on simplifications of more complex realities…. Baseball statistics are interesting not because they answer questions for us, but because they may be used to study issues. The value of baseball statistics in identifying the greatest players is not that they answer all of the questions involved, but that they provide definitive answers to some of the questions involved, which enables us to focus on others.”
I can’t speak to the formal computations of any of these articles or studies. I can’t judge the quality of their graphs, nor the logic of their inferences. I can’t tell you if they – as I have done – cherry-picked examples that seemed to support their point. Unlike, Bill James, though, I am more inclined to take these compilations of statistics at face value than to call them “simplifications of much more complex realities,” much as quantum mechanics seems, to me, a vigorous and dynamic theoretical model more than a “caricature of a more complex underlying reality.” Discursive re-presentations of anything we wish to convey, I would counter, are fairly sophisticated social exercises rather than “crude approximations to the truth.” I grant that statistical approaches provide useful, potentially insightful or interesting ways of arranging data; I’m not sure such arrangements are either simplifications or revelations of an underlying truth exemplified by that data. Claims to “read” the bedrock truths contained in statistics rather permit us to leap forward to authoritative conclusions with no explicit locus of authorization – the question and the answer become the methodology and the peer review. Of course I’m not equipped to remonstrate against the various referents and axioms of mathematics qua systematic approach to observed natural phenomena, but I don’t need to. I’m interested, instead, in how these mathematical methods are employed, and how conscientious and forthcoming we are when employing them. To return to Bill James’s point, statistics can be a useful tool for addressing specific issues. We don’t need to arrive at absolute conclusions – I depart from Bill James again in arguing that “definitive answer” is itself a suspect claim – in order to move along to another question. Given the similar themes of Bayes’ theorem, it is not hard to imagine how Nate Silver made the leap from comparing RBIs to tracking the Senate races. What sets his technique apart from descriptive analyses? It isn’t necessary to review his computations in order to ask a few questions about his methods.
Part 12 coming today at noon…