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Why Publishing Everything Is More Effective than Selective Publishing of Statistically Significant Results

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  • Marcel A L M van Assen
  • Robbie C M van Aert
  • Michèle B Nuijten
  • Jelte M Wicherts

Abstract

Background: De Winter and Happee [1] examined whether science based on selective publishing of significant results may be effective in accurate estimation of population effects, and whether this is even more effective than a science in which all results are published (i.e., a science without publication bias). Based on their simulation study they concluded that “selective publishing yields a more accurate meta-analytic estimation of the true effect than publishing everything, (and that) publishing nonreplicable results while placing null results in the file drawer can be beneficial for the scientific collective” (p.4). Methods and Findings: Using their scenario with a small to medium population effect size, we show that publishing everything is more effective for the scientific collective than selective publishing of significant results. Additionally, we examined a scenario with a null effect, which provides a more dramatic illustration of the superiority of publishing everything over selective publishing. Conclusion: Publishing everything is more effective than only reporting significant outcomes.

Suggested Citation

  • Marcel A L M van Assen & Robbie C M van Aert & Michèle B Nuijten & Jelte M Wicherts, 2014. "Why Publishing Everything Is More Effective than Selective Publishing of Statistically Significant Results," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-5, January.
    • Handle: RePEc:plo:pone00:0084896
    DOI: 10.1371/journal.pone.0084896
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    References listed on IDEAS

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    1. Daniele Fanelli, 2010. "“Positive” Results Increase Down the Hierarchy of the Sciences," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-10, April.
    2. Joost de Winter & Riender Happee, 2013. "Why Selective Publication of Statistically Significant Results Can Be Effective," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-7, June.
    3. Emily S Sena & H Bart van der Worp & Philip M W Bath & David W Howells & Malcolm R Macleod, 2010. "Publication Bias in Reports of Animal Stroke Studies Leads to Major Overstatement of Efficacy," PLOS Biology, Public Library of Science, vol. 8(3), pages 1-8, March.
    4. Gerber, Alan S. & Green, Donald P. & Nickerson, David, 2001. "Testing for Publication Bias in Political Science," Political Analysis, Cambridge University Press, vol. 9(4), pages 385-392, January.
    5. Daniele Fanelli, 2012. "Negative results are disappearing from most disciplines and countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(3), pages 891-904, March.
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    Cited by:

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    2. Leonid Tiokhin & Minhua Yan & Thomas J. H. Morgan, 2021. "Competition for priority harms the reliability of science, but reforms can help," Nature Human Behaviour, Nature, vol. 5(7), pages 857-867, July.
    3. Dwight C. K. Tse & Jeanne Nakamura & Mihaly Csikszentmihalyi, 2022. "Flow Experiences Across Adulthood: Preliminary Findings on the Continuity Hypothesis," Journal of Happiness Studies, Springer, vol. 23(6), pages 2517-2540, August.
    4. van Aert, Robbie Cornelis Maria, 2018. "Dissertation R.C.M. van Aert," MetaArXiv eqhjd, Center for Open Science.
    5. Furukawa, Chishio, 2019. "Publication Bias under Aggregation Frictions: Theory, Evidence, and a New Correction Method," EconStor Preprints 194798, ZBW - Leibniz Information Centre for Economics.
    6. Chris Hartgerink, 2019. "Verified, Shared, Modular, and Provenance Based Research Communication with the Dat Protocol," Publications, MDPI, vol. 7(2), pages 1-19, June.

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