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We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell us about p-Hacking and Publication Bias in Online Experiments

Author

Listed:
  • Brodeur, Abel
  • Cook, Nikolai
  • Heyes, Anthony
Abstract
Amazon's Mechanical Turk is a very widely-used tool in business and economics research, but how trustworthy are results from well-published studies that use it? Analyzing the universe of hypotheses tested on the platform and published in leading journals between 2010 and 2020 we find evidence of widespread p-hacking, publication bias and over-reliance on results from plausibly under-powered studies. Even ignoring questions arising from the characteristics and behaviors of study recruits, the conduct of the research community itself erodes substantially the credibility of these studies' conclusions. The extent of the problems vary across the business, economics, management and marketing research fields (with marketing especially afflicted). The problems are not getting better over time and are much more prevalent than in a comparison set of non-online experiments. We explore correlates of increased credibility.

Suggested Citation

  • Brodeur, Abel & Cook, Nikolai & Heyes, Anthony, 2022. "We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell us about p-Hacking and Publication Bias in Online Experiments," I4R Discussion Paper Series 8, The Institute for Replication (I4R).
  • Handle: RePEc:zbw:i4rdps:8
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    References listed on IDEAS

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    1. John Horton & David Rand & Richard Zeckhauser, 2011. "The online laboratory: conducting experiments in a real labor market," Experimental Economics, Springer;Economic Science Association, vol. 14(3), pages 399-425, September.
    2. Camerer, Colin F & Hogarth, Robin M, 1999. "The Effects of Financial Incentives in Experiments: A Review and Capital-Labor-Production Framework," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 7-42, December.
    3. Francesco Guala & Luigi Mittone, 2005. "Experiments in economics: External validity and the robustness of phenomena," Journal of Economic Methodology, Taylor & Francis Journals, vol. 12(4), pages 495-515.
    4. Abel Brodeur & Nikolai Cook & Anthony Heyes, 2020. "Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics," American Economic Review, American Economic Association, vol. 110(11), pages 3634-3660, November.
    5. Andreas Ortman & Le Zhang, 2013. "Exploring the Meaning of Significance in Experimental Economics," Discussion Papers 2013-32, School of Economics, The University of New South Wales.
    6. Steven D. Levitt & John A. List, 2007. "Viewpoint: On the generalizability of lab behaviour to the field," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 40(2), pages 347-370, May.
    7. Tomáš Havránek, 2015. "Measuring Intertemporal Substitution: The Importance Of Method Choices And Selective Reporting," Journal of the European Economic Association, European Economic Association, vol. 13(6), pages 1180-1204, December.
    8. Felix Chopra & Ingar Haaland & Christopher Roth & Andreas Stegmann, 2024. "The Null Result Penalty," The Economic Journal, Royal Economic Society, vol. 134(657), pages 193-219.
    9. Abel Brodeur & Mathias Lé & Marc Sangnier & Yanos Zylberberg, 2016. "Star Wars: The Empirics Strike Back," American Economic Journal: Applied Economics, American Economic Association, vol. 8(1), pages 1-32, January.
    10. Matias D. Cattaneo & Michael Jansson & Xinwei Ma, 2020. "Simple Local Polynomial Density Estimators," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1449-1455, July.
    11. Harrison, Glenn W. & Lau, Morten I. & Elisabet Rutström, E., 2009. "Risk attitudes, randomization to treatment, and self-selection into experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 70(3), pages 498-507, June.
    12. Erik Snowberg & Leeat Yariv, 2021. "Testing the Waters: Behavior across Participant Pools," American Economic Review, American Economic Association, vol. 111(2), pages 687-719, February.
    13. Stefano DellaVigna & Elizabeth Linos, 2022. "RCTs to Scale: Comprehensive Evidence From Two Nudge Units," Econometrica, Econometric Society, vol. 90(1), pages 81-116, January.
    14. David Johnson & John Barry Ryan, 2020. "Amazon Mechanical Turk workers can provide consistent and economically meaningful data," Southern Economic Journal, John Wiley & Sons, vol. 87(1), pages 369-385, July.
    15. repec:cup:judgdm:v:5:y:2010:i:5:p:411-419 is not listed on IDEAS
    16. Isaiah Andrews & Maximilian Kasy, 2019. "Identification of and Correction for Publication Bias," American Economic Review, American Economic Association, vol. 109(8), pages 2766-2794, August.
    17. Ziliak, Stephen T. & McCloskey, Deirdre N., 2004. "Size matters: the standard error of regressions in the American Economic Review," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 33(5), pages 527-546, November.
    18. Eva Vivalt, 2019. "Specification Searching and Significance Inflation Across Time, Methods and Disciplines," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(4), pages 797-816, August.
    19. Antonio A. Arechar & Gordon T. Kraft-Todd & David G. Rand, 2017. "Turking overtime: how participant characteristics and behavior vary over time and day on Amazon Mechanical Turk," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 3(1), pages 1-11, July.
    20. Nicholas Swanson & Garret Christensen & Rebecca Littman & David Birke & Edward Miguel & Elizabeth Levy Paluck & Zenan Wang, 2020. "Research Transparency Is on the Rise in Economics," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 61-65, May.
    21. Armin Falk & Stephan Meier & Christian Zehnder, 2013. "Do Lab Experiments Misrepresent Social Preferences? The Case Of Self-Selected Student Samples," Journal of the European Economic Association, European Economic Association, vol. 11(4), pages 839-852, August.
    22. Megan L Head & Luke Holman & Rob Lanfear & Andrew T Kahn & Michael D Jennions, 2015. "The Extent and Consequences of P-Hacking in Science," PLOS Biology, Public Library of Science, vol. 13(3), pages 1-15, March.
    23. Ben Gillen & Erik Snowberg & Leeat Yariv, 2019. "Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study," Journal of Political Economy, University of Chicago Press, vol. 127(4), pages 1826-1863.
    24. Coppock, Alexander, 2019. "Generalizing from Survey Experiments Conducted on Mechanical Turk: A Replication Approach," Political Science Research and Methods, Cambridge University Press, vol. 7(3), pages 613-628, July.
    25. Yun Shin Lee & Yong Won Seo & Enno Siemsen, 2018. "Running Behavioral Operations Experiments Using Amazon's Mechanical Turk," Production and Operations Management, Production and Operations Management Society, vol. 27(5), pages 973-989, May.
    26. Berinsky, Adam J. & Huber, Gregory A. & Lenz, Gabriel S., 2012. "Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk," Political Analysis, Cambridge University Press, vol. 20(3), pages 351-368, July.
    27. 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, April.
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    Citations

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    Cited by:

    1. Ankel-Peters, Jörg & Fiala, Nathan & Neubauer, Florian, 2023. "Do economists replicate?," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 219-232.
    2. Tobias Thomas & Dominik Straub & Fabian Tatai & Megan Shene & Tümer Tosik & Kristian Kersting & Constantin A. Rothkopf, 2024. "Modelling dataset bias in machine-learned theories of economic decision-making," Nature Human Behaviour, Nature, vol. 8(4), pages 679-691, April.

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    More about this item

    Keywords

    online crowd-sourcing platforms; Amazon Mechanical Turk; p-hacking; publication bias; statistical power; research credibility;
    All these keywords.

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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