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 jourof 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.

If you’re still here, forgive me. I don’t claim to grasp nor be in any position to explain the finer points of calculating probability, which is beyond my purview (if you have further interest, allow me to suggest any number of excellent and more expert books on the topic.) I only mention Bayesian priors in reference to Nate Silver’s methods in order to point out that he uses a method. Silver’s predictions, however you classify them, use a model to process data selected by him to arrive at a conclusion – a conclusion that is the result of his operation upon what he has chosen to pay attention to. Nate Silver, in short, is using statistical data to calculate probabilities. The forecasts he derives from those calculations we can call a variety of statistical inference. Since his Bayesian approach relies on probabilities, it may prove less helpful in systems of increasing uncertainty and complexity, when implications of given variables are not limited to known sets – a courtroom being an example of such a complex (social) setting, in which statistical data may conveniently suit a purpose more than “unveil a truth.” And yet, even within the bounds he has drawn for his analyses, Silver’s success is predicated on his competitors’ “getting it wrong,” using the same data sets with the same spectrum of outcomes. If, as Silver suggests, there is a “signal” hidden in the “noise” of statistical data (terms lifted from the lingo of electrical engineers), why can’t everyone concoct a model to predict the winners of political elections? Statistical data that feed widely varying conclusions suggest, to me, that such inferences have more in common with the rhetorical techniques used in a courtroom than with calculating how many blue cars may drive through my town on any given day. Continue reading