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How People Use Statistics

Author

Listed:
  • Pedro Bordalo
  • John J. Conlon
  • Nicola Gennaioli
  • Spencer Yongwook Kwon
  • Andrei Shleifer
Abstract
We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis by attending “bottom up” to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used, others are neglected. The model unifies biases in judgments about i.i.d. draws, such as the Gambler’s Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions about how changes in the salience of specific features should jointly shape the prevalence of these biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Bottom-up attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of the Bayes rule.

Suggested Citation

  • Pedro Bordalo & John J. Conlon & Nicola Gennaioli & Spencer Yongwook Kwon & Andrei Shleifer, 2023. "How People Use Statistics," NBER Working Papers 31631, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:31631
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    • Pedro Bordalo & John Conlon & Nicola Gennaioli & Spencer Kwon & Andrei Shleifer, 2023. "How People Use Statistics," Working Papers 699, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    References listed on IDEAS

    as
    1. Joshua Schwartzstein, 2014. "Selective Attention And Learning," Journal of the European Economic Association, European Economic Association, vol. 12(6), pages 1423-1452, December.
    2. Daniel J. Benjamin & Matthew Rabin & Collin Raymond, 2016. "A Model of Nonbelief in the Law of Large Numbers," Journal of the European Economic Association, European Economic Association, vol. 14(2), pages 515-544.
    3. Pedro Bordalo & Nicola Gennaioli & Andrei Shleifer, 2012. "Salience Theory of Choice Under Risk," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(3), pages 1243-1285.
    4. Richard H. Thaler, 2008. "Mental Accounting and Consumer Choice," Marketing Science, INFORMS, vol. 27(1), pages 15-25, 01-02.
    5. David M. Grether, 1980. "Bayes Rule as a Descriptive Model: The Representativeness Heuristic," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 95(3), pages 537-557.
    6. Pedro Bordalo & Nicola Gennaioli & Andrei Shleifer, 2013. "Salience and Consumer Choice," Journal of Political Economy, University of Chicago Press, vol. 121(5), pages 803-843.
    7. Matthew Rabin, 2002. "Inference by Believers in the Law of Small Numbers," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(3), pages 775-816.
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    11. Michael Woodford, 2020. "Modeling Imprecision in Perception, Valuation, and Choice," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 579-601, August.
    12. Dohmen, T.J. & Falk, A. & Huffman, D. & Marklein, F. & Sunde, U., 2009. "The non-use of Bayes rule: representative evidence on bounded rationality," ROA Research Memorandum 001, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    13. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    14. Pedro Bordalo & Giovanni Burro & Katherine B. Coffman & Nicola Gennaioli & Andrei Shleifer, 2022. "Imagining the Future: Memory, Simulation and Beliefs about Covid," NBER Working Papers 30353, National Bureau of Economic Research, Inc.
    15. Xiaomin Li & Colin F Camerer, 2022. "Predictable Effects of Visual Salience in Experimental Decisions and Games," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(3), pages 1849-1900.
    16. Jon Kleinberg & Annie Liang & Sendhil Mullainathan, 2017. "The Theory is Predictive, but is it Complete? An Application to Human Perception of Randomness," PIER Working Paper Archive 18-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Aug 2017.
    17. Pedro Bordalo & John J Conlon & Nicola Gennaioli & Spencer Y Kwon & Andrei Shleifer, 2023. "Memory and Probability," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(1), pages 265-311.
    18. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    19. Rema Hanna & Sendhil Mullainathan & Joshua Schwartzstein, 2014. "Learning Through Noticing: Theory and Evidence from a Field Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(3), pages 1311-1353.
    20. Mel Win Khaw & Ziang Li & Michael Woodford, 2021. "Cognitive Imprecision and Small-Stakes Risk Aversion [Linear Mapping of Numbers onto Space Requires Attention]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(4), pages 1979-2013.
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    Cited by:

    1. Ingar Haaland & Christopher Roth & Stefanie Stantcheva & Johannes Wohlfart, 2024. "Measuring What Is Top of Mind," CEBI working paper series 24-10, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    2. Sebastian Link & Andreas Peichl & Christopher Roth & Johannes Wohlfart, 2023. "Attention to the Macroeconomy," ECONtribute Discussion Papers Series 256, University of Bonn and University of Cologne, Germany.
    3. Katherine B. Coffman & Scott Kostyshak & Perihan O. Saygin & Katie Coffman, 2024. "Choosing and Using Information in Evaluation Decisions," CESifo Working Paper Series 11024, CESifo.
    4. Andre, Peter & Schirmer, Philipp & Wohlfart, Johannes, 2023. "Mental models of the stock market," SAFE Working Paper Series 406, Leibniz Institute for Financial Research SAFE.
    5. Peter Andre & Ingar Haaland & Christopher Roth & Mirko Wiederholt & Johannes Wohlfart, 2021. "Narratives about the Macroeconomy," ECONtribute Discussion Papers Series 127, University of Bonn and University of Cologne, Germany.
    6. Cappelen, Alexander W. & de Haan, Thomas & Tungodden, Bertil, 2024. "Fairness and limited information: Are people Bayesian meritocrats?," Journal of Public Economics, Elsevier, vol. 233(C).
    7. Pedro Gonzalez-Fernandez, 2024. "Belief Bias Identification," Papers 2404.09297, arXiv.org, revised Nov 2024.

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    More about this item

    JEL classification:

    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • G4 - Financial Economics - - Behavioral Finance
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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