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Sound and fury: McCloskey and significance testing in economics

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  • Kevin Hoover
  • Mark Siegler

Abstract

For more than 20 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 those economists' work. Properly used, significance tests are a valuable tool for assessing signal strength, for assisting in model specification, and for determining causal structure.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Journal of Economic Methodology.

Volume (Year): 15 (2008)
Issue (Month): 1 ()
Pages: 1-37

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Handle: RePEc:taf:jecmet:v:15:y:2008:i:1:p:1-37

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Related research

Keywords: Deidre McCloskey; Stephen Ziliak; statistical significance; economic significance; significance tests; R.A. Fisher; Neyman-Pearson testing; specification search; C10; C12; B41;

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References

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Blog mentions

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  3. Hoover & Siegler: McCloskey is Oh So Wrong About Statistical Significance (UPDATED)
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Cited by:
  1. Tatom, John, 2010. "Financial wellbeing and some problems in assessing its link to financial education," MPRA Paper 26411, University Library of Munich, Germany.
  2. Emmanouil Mentzakis & Paul McNamee & Mandy Ryan & Matthew Sutton, 2012. "Valuing Informal Care Experience: Does Choice of Measure Matter?," Social Indicators Research, Springer, vol. 108(1), pages 169-184, August.
  3. Thomas Mayer, 2012. "Ziliak and McCloskey's Criticisms of Significance Tests: An Assessment," Econ Journal Watch, Econ Journal Watch, vol. 9(3), pages 256-297, September.
  4. Kozo Mayumi & Mario Giampietro & Jesus Ramos-Martin, 2012. "Reconsideration of Dimensions and Curve Fitting Practice in View of Georgescu-Roegen’s Epistemology in Economics," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 17-35, December.
  5. Stephen T. Ziliak & Deirdre N. McCloskey, 2013. "We Agree That Statistical Significance Proves Essentially Nothing: A Rejoinder to Thomas Mayer," Econ Journal Watch, Econ Journal Watch, vol. 10(1), pages 97-107, January.
  6. Meszaros, Sandor, 2008. "Theory testing (hypothesis testing) in agricultural economics," Studies in Agricultural Economics, Research Institute for Agricultural Economics, issue 107, March.
  7. Tom Engsted, 2009. "Statistical vs. Economic Significance in Economics and Econometrics: Further comments on McCloskey & Ziliak," CREATES Research Papers 2009-17, School of Economics and Management, University of Aarhus.
  8. Deirdre N. McCloskey & Stephen T. Ziliak, 2012. "Statistical Significance in the New Tom and the Old Tom: A Reply to Thomas Mayer," Econ Journal Watch, Econ Journal Watch, vol. 9(3), pages 298-308, September.
  9. Thomas Mayer, 2012. "Ziliak and McClosky’s Criticisms of Significance Tests: A Damage Assessment," Working Papers 126, University of California, Davis, Department of Economics.
  10. Wicks, Rick, 2008. "Stylebook:Tips on Organization, Writing, and Formatting," Working Papers in Economics 295, University of Gothenburg, Department of Economics, revised 11 Jun 2008.
  11. Thomas Mayer, 2013. "Reply to Deirdre McCloskey and Stephen Ziliak on Statistical Significance," Econ Journal Watch, Econ Journal Watch, vol. 10(1), pages 87-96, January.
  12. Thomas Mayer, 2006. "The Empirical Significance of Econometric Models," Working Papers 620, University of California, Davis, Department of Economics.

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