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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.

Suggested Citation

  • Kevin Hoover & Mark Siegler, 2008. "Sound and fury: McCloskey and significance testing in economics," Journal of Economic Methodology, Taylor & Francis Journals, vol. 15(1), pages 1-37.
  • Handle: RePEc:taf:jecmet:v:15:y:2008:i:1:p:1-37
    DOI: 10.1080/13501780801913298
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Thomas Mayer, 2006. "The Empirical Significance of Econometric Models," Working Papers 620, University of California, Davis, Department of Economics.
    2. 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.
    3. Kim, Jae H. & Ji, Philip Inyeob, 2015. "Significance testing in empirical finance: A critical review and assessment," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 1-14.
    4. Emmanouil Mentzakis & Paul McNamee & Mandy Ryan & Matthew Sutton, 2012. "Valuing Informal Care Experience: Does Choice of Measure Matter?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 108(1), pages 169-184, August.
    5. repec:eee:soceco:v:70:y:2017:i:c:p:10-22 is not listed on IDEAS
    6. John S.L. McCombie & Ioana Negru, 2014. "On economic paradigms, rhetoric and the micro-foundations of macroeconomics," European Journal of Economics and Economic Policies: Intervention, Edward Elgar Publishing, vol. 11(1), pages 53-66, April.
    7. Nicolas Vallois & Dorian Jullien, 2017. "Estimating Rationality in Economics: A History of Statistical Methods in Experimental Economics," GREDEG Working Papers 2017-20, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), University of Nice Sophia Antipolis.
    8. John A. Tatom, 2010. "Financial Wellbeing and Some Problems in Assessing Its Link to Financial Education," NFI Working Papers 2010-WP-03, Indiana State University, Scott College of Business, Networks Financial Institute.
    9. Blakeley B. McShane & David Gal, 2016. "Blinding Us to the Obvious? The Effect of Statistical Training on the Evaluation of Evidence," Management Science, INFORMS, vol. 62(6), pages 1707-1718, June.
    10. 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.
    11. Meszaros, Sandor, 2008. "Theory testing (hypothesis testing) in agricultural economics," Studies in Agricultural Economics, Research Institute for Agricultural Economics, issue 107, March.
    12. Tom Engsted, 2009. "Statistical vs. Economic Significance in Economics and Econometrics: Further comments on McCloskey & Ziliak," CREATES Research Papers 2009-17, Department of Economics and Business Economics, Aarhus University.
    13. 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.
    14. 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.
    15. Nicolas Vallois & Dorian Jullien, 2017. "Estimating Rationality in Economics: A History of Statistical Methods in Experimental Economics," Working Papers halshs-01651070, HAL.
    16. Thomas Mayer, 2012. "Ziliak and McClosky?s Criticisms of Significance Tests: A Damage Assessment," Working Papers 126, University of California, Davis, Department of Economics.
    17. Sripad Motiram, 2014. "The Cult of statistical significance - A Review," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2014-038, Indira Gandhi Institute of Development Research, Mumbai, India.
    18. 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.
    19. Ilya Lokshin, 2015. "Whatever Explains Whatever: The Duhem-Quine Thesis And Conventional Quantitative Methods In Political Science," HSE Working papers WP BRP 23/PS/2015, National Research University Higher School of Economics.
    20. 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.

    More about this item

    Keywords

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

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology

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