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The cult of statistical significance. What economists should and should not do to make their data talk

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  • Walter Krämer

Abstract

This article takes issue with a recent book by Ziliak and McCloskey (2008) of the same title. Ziliak and McCloskey argue that statistical significance testing is a barrier rather than a booster for empirical research in economics and should therefore be abandoned altogether. The present article argues that this is good advice in some research areas but not in others. Taking all issues which have appeared so far of the German Economic Review and a recent epidemiological meta-analysis as examples, it shows that there has indeed been a lot of misleading work in the context of significance testing, and that at the same time many promising avenues for fruitfully employing statistical significance tests, disregarded by Ziliak and McCloskey, have not been used.

Suggested Citation

  • Walter Krämer, 2011. "The cult of statistical significance. What economists should and should not do to make their data talk," Working Paper Series of the German Council for Social and Economic Data 176, German Council for Social and Economic Data (RatSWD).
  • Handle: RePEc:rsw:rswwps:rswwps176
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    References listed on IDEAS

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    1. Ronald Bachmann & Michael C. Burda, 2010. "Sectoral Transformation, Turbulence and Labor Market Dynamics in Germany," German Economic Review, Verein für Socialpolitik, vol. 11, pages 37-59, February.
    2. Inmaculada Martínez-Zarzoso & Felicitas Nowak-Lehmann D. & Stephan Klasen & Mario Larch, 2009. "Does German Development Aid Promote German Exports?," German Economic Review, Verein für Socialpolitik, vol. 10, pages 317-338, August.
    3. Krämer Walter & Arminger Gerhard, 2011. "“True Believers” or Numerical Terrorism at the Nuclear Power Plant," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(5-6), pages 608-620, October.
    4. McCloskey, Donald N, 1985. "The Loss Function Has Been Mislaid: The Rhetoric of Significance Tests," American Economic Review, American Economic Association, vol. 75(2), pages 201-205, May.
    5. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
    6. Bettina Becker & Silke Uebelmesser, 2010. "Health Insurance Competition in Germany - the Role of Advertising," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 130(2), pages 169-194.
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. About the cult of statistical significance
      by Economic Logician in Economic Logic on 2012-03-07 20:40:00

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    Cited by:

    1. Bücker, Michael & van Kampen, Maarten & Krämer, Walter, 2013. "Reject inference in consumer credit scoring with nonignorable missing data," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 1040-1045.
    2. Hübler, Olaf, 2013. "Methods in empirical economics - a selective review with applications," Hannover Economic Papers (HEP) dp-513, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    3. Alice Neuhäuser & Maike Mischler & Graeme Ruxton & Markus Neuhäuser, 2013. "Gründe für von der Fraktionsdisziplin abweichendes Abstimmungsverhalten bei Bundestagsabgeordneten," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 7(1), pages 91-99, August.
    4. Hirschauer Norbert & Grüner Sven & Mußhoff Oliver & Frey Ulrich & Theesfeld Insa & Wagner Peter, 2016. "Die Interpretation des p-Wertes – Grundsätzliche Missverständnisse," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(5), pages 557-575, October.

    More about this item

    JEL classification:

    • B40 - Schools of Economic Thought and Methodology - - Economic Methodology - - - General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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