The Empire of Chance: How Probability Changed Science and Everyday Life, Gerd Gigerenzer, Zeno Swijtink, Theodore Porter, Lorrain Daston, John Beatty, and Lorenz Krüger
In their collaborative work, authors Gerd Gigerenzer, Zeno Swijtink, Theodore Porter, Lorrain Daston, John Beatty, and Lorenz Krüger attempt a cohesive study of how the science of statistics “transformed our ideas of nature, mind, and society.” (xiv) The first three chapters present a timeline on which the intellectual development of the science of statistics — with some consideration of its particular applications — is situated, the middle three deal with statistics in particular fields, and the last two concern broader implications of statistical analyses, ideologies, and methodologies. A central theme of the book is the idea that the science of statistics was both shaped and shaped by the sciences that it aided and that helped to develop it for their own explanatory and predictive goals. Professing to be the first of its kind, the survey offers detailed technical descriptions and examples that flesh out the mathematics and theories with which its actors are working.
The passages dealing with mid-nineteenth century debates surrounding the viability of statistical methods for physicians reminded me of S. Lochlann Jain’s criticisms of the same methods in her work, Malignant. Jain and her unlikely intellectual compatriots cite similar issues with the “numerical method” in medicine; it denies the complexity and uniqueness of the individual patient, aiming “not to cure the disease, but to cure the most possible out of a certain number” (Risueño d’Amador, 1836, 46). This results in the emotions Jain so skillfully articulates in her first-hand account as a cancer patient. Reduced to numbers, cancer sufferers are identified by the statistical methods their doctors use to diagnose and treat them. Equally concerning is the reliance of pharmaceutical companies on results from statistical studies to produce drugs that will target cancer on a broader scale, to the detriment of patients who would have benefitted from more personalized treatments. Perhaps these nineteenth century critics were not off base in their hesitancy to adopt such a dehumanizing method of handling disease.
Another bit I found particularly interesting was section 3.5, “Hybridization: the Silent Solution.” Having taken statistics and seen it in what I am now realizing was a surprising amount of my undergraduate science classes, I was struck by the fact that the statistical methods we learn as absolute and established are in fact far from it. Integral tenets to the type of statistics I was taught are, in actuality, theoretically at odds with one another, and yet, as the authors contend, “Statistics is treated as abstract truth, a monolithic logic of inductive inference.” (107) Because statistical methods are so widespread, I find it both surprising and alarming that these obvious impediments to its image as a well-established and unproblematic method of analysis are kept more or less hidden. It lead me into thinking about how oftentimes, when scientific disciplines are “successfully” mathematized, we deem them somehow more intelligible; they become more solid, their results more trust-worthy. Is this a valid logical jump to make, especially if statistics, one of the mathematical sciences that is employed most often, rests on shaky ground?