On Beginnings: Part 24

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.

I’m not yet convinced that outcomes outside of a closed system are knowable in advance. If they were, Olestra would have been marketed as a purgative, Ford Motor Company would have redesigned the Pinto, and I never would’ve sat through a vivid description of The Human Centipede. It isn’t possible to say definitively whether any course you pursue – college or otherwise – will be fruitful. Today, you might tell the pollster it has not been “worth it.” Tomorrow, it might seem like everything in your life was leading to this moment. Then again, maybe I’m wrong, and there is some positivistic truth we can attain and harness to build that better future, if we can only gather enough evidence. I wonder whose truth, and whose future, it will be?

Maybe the problem with history is precisely that we try to “learn” from it – to extrapolate futures or justify assertions based on past events – to construct “meaning” from happenstance, which we then term “progress.” This is partly how we come to define ourselves, in the characters, ideas, and values we either venerate or view with a *mote* of superiority. Likewise, attempts to imagine the future may be meaningful, not in some vague, power-of-becoming sense, but in what they reveal about our own subjective presuppositions, which necessarily determine the ways in which we collect, organize, and analyze data, and hash out what, after all, constitutes data in the first place. Perhaps this is why methodology is important – an aspect of data-gathering I never considered before getting my statistically worthless B.A. To the extent that statistical analyses may be employed to either create or diminish uncertainty, depending on the context and goals of the user, it may be more useful to think of them as yet another strategy for authorizing a certain paradigm, rather than an algorithmic gyre toward a fated singularity. We are the noisemakers, after all, and the modulators. We might draw own measures from the gutter to the stars, or leap into the abyss but find, as another great philosopher said, “it only goes up to your knees.” Maybe we need this king-of-the-mountain battle over “truths” to continually re-present our own idiosyncratic cosmographies – a perpetual audit of what we think we can control and what we know damn well we can’t control: enough “reality” to reassure us that we will make it through the day, and enough “uncertainty” to cling to the sometimes smug, sometimes despondent knowledge that we are not at the mercy of statistics, whatever we may choose. Or as liberal arts major Stephen Sondheim melodiously put it: