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Biased Beliefs About Random Samples: Evidence from Two Integrated Experiments

Author

Listed:
  • Daniel J. Benjamin

    (University of Southern California and NBER)

  • Don A. Moore

    (University of California at Berkeley)

  • Matthew Rabin

    (Harvard University)

Abstract

We report two incentivized experiments on four errors in reasoning about random samples: the Law of Small Numbers, Non-Belief in the Law of Large Numbers, exact representativeness, and “bin effects.” We control for a variety of confounds that constrain prior work, test predictions of existing models, and assess the magnitudes of the biases. By asking each participant many different questions about the same data, we disentangle the biases from possible rational alternative interpretations. We find that no coherent model could jointly rationalize people’s beliefs about random sequences with their beliefs about distributions of outcomes.

Suggested Citation

  • Daniel J. Benjamin & Don A. Moore & Matthew Rabin, 2018. "Biased Beliefs About Random Samples: Evidence from Two Integrated Experiments," GRU Working Paper Series GRU_2018_014, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    • Handle: RePEc:cth:wpaper:gru_2018_014
    as

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    File URL: https://www.cb.cityu.edu.hk/ef/doc/GRU/WPS/GRU%232018-014%20Benjamin.pdf
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    References listed on IDEAS

    as
    1. Matthew Rabin & Dimitri Vayanos, 2010. "The Gambler's and Hot-Hand Fallacies: Theory and Applications," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 730-778.
    2. Joshua B. Miller & Adam Sanjurjo, 2015. "Surprised by the Gambler’s and Hot Hand Fallacies? A Truth in the Law of Small Numbers," Working Papers 552, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
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    Cited by:

    1. Ingar Haaland & Christopher Roth & Johannes Wohlfart, 2023. "Designing Information Provision Experiments," Journal of Economic Literature, American Economic Association, vol. 61(1), pages 3-40, March.
    2. Victor Stango & Jonathan Zinman, 2019. "We Are All Behavioral, More or Less: Measuring and Using Consumer-Level Behavioral Sufficient Statistics," Working Papers 19-14, Federal Reserve Bank of Philadelphia.
    3. Little, Andrew T., 2022. "Information Theory and Biased Beliefs," OSF Preprints vfqy2, Center for Open Science.
    4. Eyting, Markus & Schmidt, Patrick, 2021. "Belief elicitation with multiple point predictions," European Economic Review, Elsevier, vol. 135(C).

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    More about this item

    Keywords

    Law of Small Numbers; Gambler’s Fallacy; Non-Belief in the Law of Large Numbers; Big Data; Support Theory;
    All these keywords.

    JEL classification:

    • B49 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Other

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