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 13

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 makes it clear that his preference for Bayes’ theorem is a conscientious nod to the role of uncertainty in any depiction of the future.  His attempts to mitigate uncertainty, we can imagine, are addressed in his model; his accounting for it is evident in the probabilities in which he lays out his forecasts, acknowledging some estimate of chance either way of getting it right or getting it wrong – if we could say, in a sense, that Nate Silver could be “wrong.”  Because each candidate’s odds are expressed in probabilities, Silver calculates his misses into his projections.  You could say when an election result falls within the minority of his probability set, it’s a playing out of what he had always acknowledged was a possibility – which is just about the best form of hedging anyone might devise.  And yet, his renown turns on his getting it right where others were mistaken – using polling data available to all of the pundits attempting to forecast the election results.  So what gives?  Why is it so difficult to find the “signal” within the “noise?” Continue reading

On Beginnings: Part 12

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 methodology, to the extent he describes it, seems to be stitching together a few different types of analyses.  His aims and limits are fairly well defined:  Forecast the results of elections in various states in which a nomination process whittles the initial field of candidates down to a handful.  For each open seat, one candidate will triumph.  Silver then sets about combining political polling data, from which he derives the probabilities of his forecasts, the way you would bet on Natter Phineas to win at 6:1.  We could stop there, and chalk his success up to the inviolable laws of math.  Or we could ask a few more questions.  After all, elections don’t offer a clean analogy to sporting events, or poker, or red cars vs. blue cars.  How often are elections contests between equals?  Or contests, for that matter, at all?  I voted last week.  Of the near-dozen positions on the ballot, two were contested.  A closer examination of the 2008 senatorial election results suggests there were a great many seats in the U.S. Senate in which the incumbent was not challenged aggressively, or at all.  I don’t mean to diminish Nate Silver’s achievements.  I just want to point out that many candidates have ostensible advantages in our elections, making any survey of individual odds less clear cut than it might appear at the outset.  Nate Silver’s data analyses may work, then, because he doesn’t solely rely on them.  There are other sources of data, after all, for deriving an estimate of a candidate’s ephemeral popularity.  When polling data does not inspire an adequate level of confidence, or when the results yield no statistically significant edge to any party, you might turn to the effects of factors that may or may not be reflected in the polling data:  the exigencies of re-districting lines, political alliances and legacies, successful state and federal social or economic programs, popular scandals, infamous gaffes, (it’s so hard to choose), or any other histrionic hoo-ha of our political stage that invites the intrepid to nestle up to the glow of a wider world’s bad reality tv.

What happened this year?

Part 13 coming tomorrow morning…

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 8

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.

 

In the aftermath of our 2007 financial crisis, it does seem rational for a more debt-savvy general public to be concerned with ROI, particularly when we see that college costs have surged 500% since 1985 – though we are given little context in which to ascertain how costs have risen, in the face of what types of budget cuts, at what institutions, in what forms, and how that additional money appears in the ledgers of either public or private two- or four- year institutions nationwide.  I’m not refuting the possibility that the numbers are true – tuition certainly feels like it’s risen over the past decade, but these claims are difficult to assess at face value, given I could say I have grown over 117% taller since 1985.  Continue reading

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 6

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 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

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