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 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