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:

“Who knows what may
be lurking on the journey?
Into the woods
To get the thing
That makes it worth
the journeying.”

On Beginnings: Part 23

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]rrational behavior in the markets may result precisely because individuals are responding rationally to their incentives.”  The Signal and the Noise, p.357.

Surely the incentives for generating $tati$tical analy$e$ are the same incentives I have to keep sweeping up crayons.  Molly Ball says it better:

“[P]ollsters get paid by the poll, ad makers by the ad, phone-calling firms by the call, direct-mailers by the piece. They all have an incentive to promote their services, whether or not doing so helps the campaign win—and they face few consequences if it doesn’t.”

Nate Silver’s success depends upon him nailing his forecasts.  Other types of analyses may be funded whether they prove correct or no.  We can assume, given the uncertainty into which these predictions are cast, they aren’t as concerned with whether or not the statistics of an unknown future will vindicate them, as they are with the more immediate demands of saying something interesting, selling more copy, meeting a deadline, responding to a critique – i.e., getting about the business of circumlocution that constitutes the ever-changing public discourse in which we participate and by so doing, authorize.  I can’t debunk them – they might be right.  Like Fukuyama, they’ve chosen premises that aren’t falsifiable.  But as producers/consumers of this information, I think we could afford to be a bit more skeptical about the weight we give a statistical projection of a job market two, three, or five years into the futureI also just want to point out that we’re doing it – that inferences we represent as meaningful are simultaneously explanations of mathematical correlations and assertions of mathematical correlations.  And if you’re like me, it’s tempting to let these assertions inform the decisions we make when we ponder the costs and benefits of choosing an educational or career path, thanks to our procrustean tendencies to apply blanket abstractions to individuals.  But if I can’t assume I know anything about the creative processes of corporate CEOs based on my own stereotypes, maybe I likewise shouldn’t hold myself to projections of an unknown future based on deductions retrospectively drawn from a range of data within an arbitrary set of criteria. In the end, I am being imagined, too – just as the notion of a past is an imaginative exercise, and the assertion of a future based upon it is an extension of that conceit.  These will likely always be contested, unless Fukuyama’s History plays out to its natural End – or we could say:  someone else’s Beginning, Middle, or Tuesday.  I’m ready to believe in the possibility of anything, now that we’ve invented Furbies.  When faced with the showdown between “truth in the data” vs. “common sense,” I’ll put my money on the even chance that we’re making it up as we go along.  For “what is now proved was once only imagined,” so sayeth my favorite 18th century Englishman.  I’m with you, liberal arts major Newt Gingrich.  Let’s colonize this moon.  I bet I could get hosed there off two beers.

Part 24 coming today at noon…

On Beginnings: Part 18

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.

 

“The signal is the truth. The noise is what distracts us from the truth.”  The Signal and the Noise, p.17.

I wonder how the truth would appear when we found it, and who would validate it once and for all?  Should we assume collections of data “contain” essential “truths” about our own behavior that we can use to reliably predict our future choices?  Or do such statistical and probabilistic applications just plow little furrows where we nurture some irreproachable Mr. Potato Head of signification, permitting us to place and re-place any provisional features?  If so, it might benefit us to be mindful of the selections we make when, for instance, we cite probabilities to plug the gap between indecision and action.  If we place too much trust in what we perceive to be the predictive power of statistical inferences, we may be nonplussed when they inevitably fail to provide exactly the information we think we need.  Then again, there are plenty of times when we conveniently ignore information, statistical or otherwise, when it is at cross purposes to our agenda.  We may allow statistical inference to drive our behavior in ways not otherwise rational; or poo-poo marginal conclusions based on statistical surveys that turned out to have some merit, after all.

Data analyses may also lead us to make inferences not substantiated by the numbers, as in this little gem, describing another PayScale study:

“All these caveats are true. But overall, the PayScale study surely overstates the financial value of a college education. It does not compare graduates’ earnings to what they would have earned, had they skipped college. (That number is unknowable.) It compares their earnings to those of people who did not go to college—many of whom did not go because they were not clever enough to get in. Thus, some of the premium that graduates earn simply reflects the fact that they are, on average, more intelligent than non-graduates.”

I would be interested to see the data that rendered concrete notions of what constitutes intelligence, who has it, who doesn’t, what varieties they possess, and in what measure.  I suspect we can’t easily determine such things.  Or rather, we all can, and freely do.

Part 19 coming tomorrow morning…

On Beginnings: Part 14

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. Continue reading

On Beginnings: Part 11

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.” Continue reading

On Beginnings: Part 10

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.

 

Representations of “truths” bolstered by statistical data need not be deliberately misleading to be subject to tendencies B.A. holder Darrel Huff called “statisticulation.”  Any data we might collect is a product of the question we ask, and the ways in which we seek to answer that question.  Of course we are “manipulating” the numbers – we can’t help doing so in the process of gathering bits we deem relevant into the little pile we use to build up notions of order, character, and causality.  Is there a way around this dilemma?  Depends on whom you ask:  Modern, silicon-based engines of technology, some contend, have rendered obsolete the traditional vetting cycle of facts – observe, hypothesize/model, test – by providing more precise methods of measurement and a vast interconnected repository for storing and sharing data.  Data no longer needs a model, in this approach – it can be collected and analyzed statistically by high-speed processors.  The “End of Theory,” as Chris Anderson opined in 2008, may be nigh, allowing scientists to kick back with a margarita while pattern-detecting algorithms process data that will autogenerate yet-unknown points of inquiry – no never-adequate scientific model required, in all its messy human-ness.  You could call this Big Data method progress.  You could call it a pendulum swing.  Or you could call it a waste of time to contrast to classical methods, since no one actually believes data is worth collecting for the sake of collection alone, with no defined mechanism for selecting and interpreting it.  Who would store all that?  Besides the NSA? Continue reading

On Beginnings: Part 9

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.

 

If statistics are so handy for gauging the value of a college degree, why do we encounter such a disorienting array of interpretations?  And what are the methods used to collect the data from which we extrapolate?  Publishers of these studies often provide access to descriptions of methodologies, along with standard error rates and explanatory endnotes.  We can forego, in this case, the tedious and contingent process of tabulating probabilities; the data itself testifies to some past or projected “reality,” and need only be arrayed by percentages into neat columns or colorful graphs to serve as convenient indices of our implicit options and their fiscal consequences.  This allows us to parse out the benefits of various types and levels of education like sifting through colored beads in a jar.  The results are then re-viewed by various media and jettisoned into the sphere of either popular consumption or popular indifference.  Many reviewers seem reluctant to place any real or imagined value on a college degree, but as liberal arts major Hugh Hefner can tell you, when you punctuate the text with compelling pictures, only the most dedicated readers are going to probe the articles.  I can’t help contrasting the statistics collected in these studies to the polling data Nate Silver mines to polish his predictions.  Election polling data purport to show the number of people opting for a particular candidate in a given election at a specific moment in time; descriptive statistical data on education and career choices seem to provide license to further deduce which majors are worth it, and which ones are worthless.

Part 10 coming today at noon…

On Beginnings: Part 7

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.

 

Well….

Educational and vocational statistics can be compiled by many different official or unofficial entities, including private information service companies like Payscale, non-profit, trust-funded research institutions like the Pew Research Center, and government agencies like the Bureau of Labor Statistics, the U.S. Census Bureau, and the National Center for Education Statistics.

The extrapolations from these statistics can be done by the agencies themselves, but are also done in the open market of public discourse, and in the Great Debate Over the Value of a College Degree, seem to fall under a few general rubrics:  the rift over the utility of any college degree and how to determine such, and another point at which advocates seem to split off into college-specific camps – some espousing the virtues of a more versatile liberal arts education, some suggesting the irrelevance of the liberal arts in a modern world of applied and technical sciences.  The standards of measure are vaguely economic – comparing for instance, median salaries and expected earnings of graduates, projected job growth by sector, and a catalog of accrued assets vs. debt, by field of study.  Since I was a liberal arts major, I naturally take a keen interest in these debates, as I, too, struggle to estimate the value of my degree weighed against my deflated ambitions to join the Big Top. Continue reading

On Beginnings: Part 5

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.

 

Are there other areas where the application of Bayesian inference might seem more dubious than Nate Silver – who offers it as a universal method of assessing data – suggests?  Let’s venture away from numbery things and into, say, the proceedings of a criminal trial, where an impartial judge mediates a contest of vignettes between professional raconteurs before an audience of peers tasked with deciding which version of justice is the just-iest..  A criminal trial sounds straightforward in theory, and a fair candidate for Bayesian inference.  A criminal trial in practice, however, is rarely so amenable to strict forms of logic.  Continue reading

On Beginnings: Part 3

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.

 

Statistical inference and probability can employ as masochistic a level of computation as the user wishes to pursue, but we can look at some basic principles that will move the conversation along without exceeding the ten-digit limit of nature’s abacus.  Statistics, broadly defined, is a branch of the formal sciences that deals with the collection, organization, and analysis of data.  Data, for our purposes, may be anything we wish to define as objects of our attention.  When you sit on a curb licking a Push Pop and counting blue cars vs. red cars, you are gathering and cataloging statistical data.  We often use this data to infer what we may not directly observe – that is, to use our sample to posit a broader statement:  “I think there are more red cars than blue cars in this town.”  Our conclusion is an example of inductive reasoning, in which bits of information are collected and used to formulate a general proposition.  This is where probability comes into play, to gauge the likelihood of our hypothesis holding up in the face of new information – or, just as often, to turn a profit on a more heuristic, casually calculated gamble:  “I bet you two Skittles there are more red cars than blue cars.”  The confidence you place on such a bet can be assigned a numeric value based on the colors of various cars you have already tallied – kindling, in turn, any spark of compunction in your pals.  Probability, in a textbook sense, is an expression of the chance ascribed to a specific event against all possible events within a fixed set of conditions:  rolling a die, for example, which has a 1 in 6 chance of landing on any given face.  Continue reading