Biased Beliefs About Random Samples: Evidence from Two Integrated Experiments
Don A. Moore () and
Matthew Rabin ()
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Don A. Moore: University of California at Berkeley
Matthew Rabin: Harvard University
No GRU_2018_014, GRU Working Paper Series from City University of Hong Kong, Department of Economics and Finance, Global Research Unit
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.
Keywords: Law of Small Numbers; Gambler’s Fallacy; Non-Belief in the Law of Large Numbers; Big Data; Support Theory (search for similar items in EconPapers)
JEL-codes: B49 (search for similar items in EconPapers)
Pages: 65 pages
New Economics Papers: this item is included in nep-exp
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Working Paper: Biased Beliefs About Random Samples: Evidence from Two Integrated Experiments (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:cth:wpaper:gru_2018_014
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