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A Primer on the ‘Reproducibility Crisis’ and Ways to Fix It

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  • W. Robert Reed

Abstract

This article uses the framework of Ioannidis () to organise a discussion of issues related to the ‘reproducibility crisis’. It then goes on to use that framework to evaluate various proposals to fix the problem. Of particular interest is the ‘post†study probability’, the probability that a reported research finding represents a true relationship. This probability is inherently unknowable. However, a number of insightful results emerge if we are willing to make some conjectures about reasonable parameter values. Among other things, this analysis demonstrates the important role that replication can play in improving the signal value of empirical research.

Suggested Citation

  • W. Robert Reed, 2018. "A Primer on the ‘Reproducibility Crisis’ and Ways to Fix It," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 51(2), pages 286-300, June.
    • Handle: RePEc:bla:ausecr:v:51:y:2018:i:2:p:286-300
    DOI: 10.1111/1467-8462.12262
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    References listed on IDEAS

    as
    1. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    2. Eric W. Djimeu & Deo-Gracias Houndolo, 2016. "Power calculation for causal inference in social science: sample size and minimum detectable effect determination," Journal of Development Effectiveness, Taylor & Francis Journals, vol. 8(4), pages 508-527, October.
    3. W. Robert Reed, 2017. "Replication in labor economics," World of Labour, LISER, pages 413-413, December.
    4. Zacharias Maniadis & Fabio Tufano & John A. List, 2017. "To Replicate or Not To Replicate? Exploring Reproducibility in Economics through the Lens of a Model and a Pilot Study," Economic Journal, Royal Economic Society, vol. 127(605), pages 209-235, October.
    5. Camerer, Colin & Dreber, Anna & Forsell, Eskil & Ho, Teck-Hua & Huber, Jurgen & Johannesson, Magnus & Kirchler, Michael & Almenberg, Johan & Altmejd, Adam & Chan, Taizan & Heikensten, Emma & Holzmeist, 2016. "Evaluating replicability of laboratory experiments in Economics," MPRA Paper 75461, University Library of Munich, Germany.
    6. Zacharias Maniadis & Fabio Tufano & John A. List, 2017. "To Replicate or Not To Replicate? Exploring Reproducibility in Economics through the Lens of a Model and a Pilot Study," Economic Journal, Royal Economic Society, vol. 127(605), pages 209-235, October.
    7. Jennifer L. Castle & Xiaochuan Qin & W. Robert Reed, 2013. "Using Model Selection Algorithms To Obtain Reliable Coefficient Estimates," Journal of Economic Surveys, Wiley Blackwell, vol. 27(2), pages 269-296, April.
    8. Daniel J. Benjamin & James O. Berger & Magnus Johannesson & Brian A. Nosek & E.-J. Wagenmakers & Richard Berk & Kenneth A. Bollen & Björn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Chr, 2018. "Redefine statistical significance," Nature Human Behaviour, Nature, vol. 2(1), pages 6-10, January.
      • Daniel Benjamin & James Berger & Magnus Johannesson & Brian Nosek & E. Wagenmakers & Richard Berk & Kenneth Bollen & Bjorn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Christopher Chambe, 2017. "Redefine Statistical Significance," Artefactual Field Experiments 00612, The Field Experiments Website.
    9. Lucas C. Coffman & Muriel Niederle, 2015. "Pre-analysis Plans Have Limited Upside, Especially Where Replications Are Feasible," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 81-98, Summer.
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