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.
“Make it seem inevitable,” Louis Pasteur advised his students preparing to publish their research, in the oft-cited apocryphal chestnut. When we present statistical data as though the data itself harbored some perfect implicit revelation, we are doing just that. When the data “misleads” us, we are doing that yet again. Even the polling data Nate Silver relies on is subject to our vacillations between obstinate fealty and obstinate skepticism. There are times, of course, when polls really do get it wrong, but it doesn’t seem to affect their credibility until the results clash with our agenda. Or when elections that don’t turn out like we want can be deemed “flawed.” We laud the numbers when it suits our purposes, then call compilations of those numbers tainted when they produce outcomes we consider undesirable. Is the data “bad?” Did we collect it imperfectly, or imperfectly interpret perfectly true information? Are we wishy-washy? Or is this just how we shimmy through life, alternately contesting and consenting in the service of our momentary aims? Do we hold static views in a mutable world? If we did, we wouldn’t have to take polls so compulsively – but we’re fickle. We’re duplicitous. We strategize. Even with a constant showing of hands, a constant checking-in, political polls aren’t a reliable indication of an election outcome.
Likewise, statistical analyses don’t exist in a vacuum; they comprise data we use to construct a portrait of physical events. As such, they cannot be separated from the same sorts of contexts and motivations that fuel any other form of popular discourse The indiscriminate invocation of their “mathiness,” I would argue, is used to lend the same sort of legitimacy to interested assertions that vague appeals to any sort of external authority-granting body do. More interesting than the results, or even the methodologies of the college-evaluation studies we’ve reviewed, is the assumption that we can reach hard and fast conclusions about topics so contingent and individually variable, with each side of the debate projecting a cool, detached scientific control I bet we don’t have. Perhaps it would be useful, then, to view applications of statistical data in certain contexts as rhetorical techniques that employ a mathematical expression, rather than an exegesis of a truth revealed in the numbers. Numbers, like language, can be ambiguous – of course, 1 is 1 is 1, as any mathematician will tell you. But “1” as a measure of quantity ceases to be strictly mathematical. “I have $1 in my bank account” sounds pretty innocuous. But a poll conducted at a university I visited years ago proudly stated on table cards: “9 out of 10 guys at [our university] think that when a girl says no, it means no!” Well, now that “1” has a bit different meaning. But what meaning should we give it?
Part 15 coming tomorrow morning…