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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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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
Date of revision:
Handle: RePEc:dkn:econwp:eco_2009_13
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  1. 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.
  2. Joan Costa-Font & Marin Gemmill & Gloria Rubert, 2008. "Re-visiting the Health Care Luxury Good Hypothesis: Aggregation, Precision, and Publication Biases?," Working Papers in Economics 197, Universitat de Barcelona. Espai de Recerca en Economia.
  3. 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.
  4. T. D. Stanley, 2008. "Meta-Regression Methods for Detecting and Estimating Empirical Effects in the Presence of Publication Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(1), pages 103-127, 02.
  5. Hristos Doucouliagos & T. D. Stanley, 2009. "Publication Selection Bias in Minimum-Wage Research? A Meta-Regression Analysis," British Journal of Industrial Relations, London School of Economics, vol. 47(2), pages 406-428, 06.
  6. 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.
  7. J. Bradford De Long & Kevin Lang, . "Are All Economic Hypotheses False?," J. Bradford De Long's Working Papers _117, University of California at Berkeley, Economics Department.
  8. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
  9. 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.
  10. J. Copas, 1999. "What works?: selectivity models and meta-analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 95-109.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
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