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

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  • Stanley, T. D.
  • Jarrell, Stephen B.
  • Doucouliagos, Hristos

Abstract

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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Bibliographic Info

Article provided by American Statistical Association in its journal The American Statistician.

Volume (Year): 64 (2010)
Issue (Month): 1 ()
Pages: 70-77

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Handle: RePEc:bes:amstat:v:64:i:1:y:2010:p:70-77

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References

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  1. 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.
  2. Joan Costa-i-Font & Marin Gemmill & Gloria Rubert, 2009. "Re-visiting the health care luxury good hypothesis: aggregation, precision, and publication biases?," LSE Research Online Documents on Economics 25303, London School of Economics and Political Science, LSE Library.
  3. 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.
  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.
  5. 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.
  6. Andrew Rose, 2004. "A Meta-Analysis of the Effect of Common Currencies on International Trade," NBER Working Papers 10373, National Bureau of Economic Research, Inc.
  7. 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.
  8. 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.
  9. Lovell, Michael C, 1983. "Data Mining," The Review of Economics and Statistics, MIT Press, vol. 65(1), pages 1-12, February.
  10. 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.
  11. 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.
  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.
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Cited by:
  1. Katrin Auspurg & Thomas Hinz, 2011. "What Fuels Publication Bias? Theoretical and Empirical Analyses of Risk Factors Using the Caliper Test," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(5-6), pages 636-660, November.
  2. Stern, David & Enflo, Kerstin, 2013. "Causality Between Energy and Output in the Long-Run," Lund Papers in Economic History 126, Department of Economic History, Lund University.
  3. T.D. Stanley & Hristos Doucouliagos, 2013. "Better than Random: Weighted Least Squares Meta-Regression Analysis," Economics Series 2013_2, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  4. T.D. Stanley & Hristos Doucouliagos, 2011. "Meta-Regression Approximations to Reduce Publication Selection Bias," Economics Series 2011_4, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  5. Chris Doucouliagos & T.D. Stanley, 2013. "Are All Economic Facts Greatly Exaggerated? Theory Competition And Selectivity," Journal of Economic Surveys, Wiley Blackwell, vol. 27(2), pages 316-339, 04.
  6. Nelson, Jon P., 2014. "Estimating the price elasticity of beer: Meta-analysis of data with heterogeneity, dependence, and publication bias," Journal of Health Economics, Elsevier, vol. 33(C), pages 180-187.
  7. T.D. Stanley & Hristos Doucouliagos, 2013. "Neither Fixed nor Random: Weighted Least Squares Meta-Analysis," Economics Series 2013_1, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
  8. Tomas Havranek & Ondrej Kokes, 2013. "Income Elasticity of Gasoline Demand: A Meta-Analysis," Working Papers IES 2013/02, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Apr 2013.
  9. Doucouliagos, Chris & Stanley, T.D. & Giles, Margaret, 2012. "Are estimates of the value of a statistical life exaggerated?," Journal of Health Economics, Elsevier, vol. 31(1), pages 197-206.
  10. Doucouliagos, Hristos & Stanley, T.D. & Viscusi, W. Kip, 2014. "Publication selection and the income elasticity of the value of a statistical life," Journal of Health Economics, Elsevier, vol. 33(C), pages 67-75.

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