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Abstract
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State lotteries—to take just 1 phenomenon in which probabilistic thinking intersects with everyday life—are described aphoristically as a tax on people who are bad at mathematics. It would be more precise to call them a tax on people who are bad at math but act on it anyway. In the space between an understanding of conditional probability and the need (or desire) for consequential action, much depends on whether one perceives a connection between these realms, forces a connection, or accepts with equanimity that one may not exist.
There is often an uneasy tension, suggest specialists in statistics, epidemiology, and emergency medicine, between quantitative thinking and the broader forms of cognition that add up to sound clinical practice. Quantifying risk is a difficult process for most people to comprehend, journalists in particular and physicians not excepted. Proponents of evidence-based medicine, although working to separate comprehensible information from the workings of chance and the influences of wishful thinking or material interest, acknowledge that epidemiologic correlations, clinical courses, and treatment outcomes are all too complex for reduction to a P value or a binary choice.
Yet procedures that substitute those deceptively simple, easily biased statistical values for more nuanced modes of knowledge and more meaningful criteria permeate much of the research literature—even a majority of it, according to the analyses of John P. A. Ioannidis, MD, PhD, professor of medicine, epidemiology, and statistics at Stanford School of Medicine. By the time research is translated into headline language, a rough scientific equivalent of the economic observation termed Gresham's Law (“bad money drives out good if exchanged at the same price”) may be in operation.
Just as methodological errors migrate from professional journals into the lay press so also do methodological critiques. A New York Times op-ed recently explored the general question of the reliability of research reports, particularly when left uncontested by replication studies, citing the “de-discovery” efforts of Columbia University virologist W. Ian Lipkin, MD. A profile in The Atlantic last year brought Dr. Ioannidis's research to a lay readership's attention. In the wake of Dr. Ioannidis's work, the popular campaign by Yale statistician and information designer Edward Tufte, PhD, against junk statistics and “chartjunk” and a spate of prominent reversals of professional consensus about risk factors, cancer-screening outcomes, and once-trusted treatments, physicians may face increasing public skepticism, shading off into cynicism, about whether clinical procedures and decisions rest on solid ground.
Language: en |