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Could It Be Better to Discard 90% of the Data? A Statistical Paradox

  • T.D. Stanley

    ()

  • Stephen B. Jarrell

    ()

  • Hristos Doucouliagos

    ()

Conventional practice is to draw inferences from all available data and research results, even though there is ample evidence to suggest that empirical literatures suffer from publication selection bias. When a scientific literature is plagued by such bias, a simple discarding of the vast majority of empirical results can actually improve statistical inference and estimation. Simulations demonstrate that, if the majority of researchers, reviewers, and editors use statistical significance as a criterion for reporting or publishing an estimate, discarding 90% of the published findings greatly reduces publication selection bias and is often more efficient than conventional summary statistics. Improving statistical estimation and inference through removing so much data goes against statistical theory and practice; hence, it is paradoxical. We investigate a very simple method to reduce the effects of publication bias and to improve the efficiency of summary estimates of accumulated empirical research results that averages the most precise ten percent of the reported estimates (i.e., ‘Top10’). In the process, the critical importance of precision (the inverse of an estimate’s standard error) as a measure of a study’s quality is brought to light. Reviewers and journal editors should use precision as one objective measure of a study’s quality.

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File URL: http://www.deakin.edu.au/buslaw/aef/workingpapers/papers/2009_13.pdf
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Paper provided by Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance in its series Economics Series with number 2009_13.

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Date of creation: 16 Sep 2009
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Handle: RePEc:dkn:econwp:eco_2009_13
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  1. Rose, Andrew K, 2003. "A Meta-Analysis of the Effect of Common Currencies on International Trade," CEPR Discussion Papers 4341, C.E.P.R. Discussion Papers.
  2. De Long, J Bradford & Lang, Kevin, 1992. "Are All Economic Hypotheses False?," Journal of Political Economy, University of Chicago Press, vol. 100(6), pages 1257-72, December.
  3. Laroche, P., 2000. "What do Unions do to Productivity? A Meta-Analysis," Papers 2000-5, Groupe de recherche en économie financière et en gestion des entreprises, Universite Nancy 2.
  4. T.D Stanley & Hristos Doucouliagos, 2007. "Identifying and Correcting Publication Selection Bias in the Efficiency-Wage Literature: Heckman Meta-Regression," Economics Series 2007_11, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
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  6. Feige, Edgar L, 1975. "The Consequences of Journal Editorial Policies and a Suggestion for Revision," Journal of Political Economy, University of Chicago Press, vol. 83(6), pages 1291-95, December.
  7. Hristos Doucouliagos & T.D. Stanley, 2008. "Publication Selection Bias in Minimum-Wage Research? A Meta-Regression Analysis," Economics Series 2008_14, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  8. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
  9. Chris Doucouliagos & Patrice Laroche, 2007. "Unions and Profitability: A Meta-Analysis," Economics Series 2007_01, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  10. Costa-Font, J & Gemmill M & Rubert G, 2009. "Re-visiting the Health Care Luxury Good Hypothesis: Aggregation, Precision, and Publication Biases?," Health, Econometrics and Data Group (HEDG) Working Papers 09/02, HEDG, c/o Department of Economics, University of York.
  11. T. D. Stanley, 2001. "Wheat from Chaff: Meta-analysis as Quantitative Literature Review," Journal of Economic Perspectives, American Economic Association, vol. 15(3), pages 131-150, Summer.
  12. Eric Krassoi Peach & T. Stanley, 2009. "Efficiency Wages, Productivity and Simultaneity: A Meta-Regression Analysis," Journal of Labor Research, Springer, vol. 30(3), pages 262-268, September.
  13. T.D. Stanley, 2006. "Meta-Regression Methods for Detecting and Estimating Empirical Effects in the Presence of Publication Selection," Economics Series 2006_20, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  14. Card, David & Krueger, Alan B, 1995. "Time-Series Minimum-Wage Studies: A Meta-analysis," American Economic Review, American Economic Association, vol. 85(2), pages 238-43, May.
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