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Sound and Fury: McCloskey and Significance Testing in Economics

By Kevin D. Hoover and Mark V. Siegler


For about twenty years, Deidre McCloskey has campaigned to convince the economics profession that it is hopelessly confused about statistical significance. She argues that many practices associated with significance testing are bad science and that most economists routinely employ these bad practices: “Though to a child they look like science, with all that really hard math, no science is being done in these and 96 percent of the best empirical economics. . .” (McCloskey 1999). McCloskey’s charges are analyzed and rejected. That statistical significance is not economic significance is a jejune and uncontroversial claim, and there is no convincing evidence that economists systematically mistake the two. Other elements of McCloskey’s analysis of statistical significance are shown to be ill-founded, and her criticisms of practices of economists are found to be based in inaccurate readings and tendentious interpretations of their work. Properly used, significance tests are a valuable tool for assessing signal strength, for assisting in model specification, and for determining causal structure.statistical significance, economic significance, significance testing, regression analysis, econometric methodology, Deirdre McCloskey, Neyman-Pearson testing

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