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Statistical Significance

The sciences, or subfields within each science, divide into those which have kept a focus on How Big and those which have not. No geomorphologist, for example, would rest satisfied that "there exists" an effect of rainfall on the height of the land: she wants quantitative estimates of the rate of mechanical denudation . . . [continues]

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[Continued from above] The sciences, or subfields within each science, divide into those which have kept a focus on How Big and those which have not. No geomorphologist, for example, would rest satisfied that "there exists" an effect of rainfall on the height of the land: she wants quantitative estimates of the rate of mechanical denudation . . . continues in millimeters per century. No physicist is much interested in the "statistical significance" at conventional levels between exact IA calculations as against pole approximations of the electromagnetic form factor at high values of four-momentum transfer squared: he wants to see the quantitative difference in a simulation and argue that it matters for the science. No historian would be comfortable with a claim that German migration to the United States "was a factor" in the election of 1860: she would want to know how much. What matters to science is oomph, every time. Unhappily, in many fields of science the matter of How Much has been lost, commonly by a confusion between actual scientific measurement on the one hand and philosophical absolutes on the other. One counterexample to Goldbach's Conjecture (that every even number can be expressed as the sum of two primes: 20 = 13 + 7) would suffice to kill it for good in the Department of Mathematics. Yet it would still go on being useful to engineers devising computer locks, since no counterexample has been found for numbers up into the billions. Existence, arbitrary statistical significance, philosophical possibilities uncalibrated to the sizes of important effects in the world are useless for science. Yet in medical science, in population biology, in much of sociology, political science, psychology, and economics, in parts of literary study, there reigns the spirit of the Mathematics or Philosophy Departments (appropriate in their own fields of absolutes). The result has been a catastrophe for such sciences, or former sciences. The solution is simple: get back to seeking oomph. It would be wrong, of course, to abandon math or statistics. But they need every time to be put into a context of How Much, as they are in chemistry, in most biology, in history, and in engineering science.

ORDER INFO In The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (University of Michigan Press, 2007), McCloskey and Stephen Ziliak show that statistical signficance, though routine in economics, psychology, medicine, and increasingly in the courts, is nonsense when used alone (as it almost invariably is), and has been known to be nonsense in this partial use since it was invented — often by the very inventors, such as Edgeworth and Gosset (the "Student" of Student's-t).

Deirdre N. McCloskey is Distinguished Professor of Economics, History, English, and Communication at the University of Illinois at Chicago;
and Professor of Economic History, Gothenburg University, Sweden

Prudentia      Luis Abrigo, Editor

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