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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
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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;

    JEL classification:

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

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