Teaching Bayesian reasoning in less than two hours.

Sedlmeier, Peter, Gigerenzer, Gerd·2001·Journal of Experimental Psychology: General

Abstract

The authors present and test a new method of teaching Bayesian reasoning, something about which previous teaching studies reported little success. Based on G. Gigerenzer and U. Hoffrage's (1995) ecological framework, the authors wrote a computerized tutorial program to train people to construct frequency representations (representation training) rather than to insert probabilities into Bayes's rule (rule training). Bayesian computations are simpler to perform with natural frequencies than with probabilities, and there are evolutionary reasons for assuming that cognitive algorithms have been developed to deal with natural frequencies. In 2 studies, the authors compared representation training with rule training; the criteria were an immediate learning effect, transfer to new problems, and long-term temporal stability. Rule training was as good in transfer as representation training, but representation training had a higher immediate learning effect and greater temporal stability.

Cites (66)

  1. Judgment under Uncertainty: Heuristics and Biases
    Tversky, Amos, Kahneman, Daniel · 1975 · Utility, Probability, and Human Decision Making
  2. A power primer.
    Cohen, Jacob · 1992 · Psychological Bulletin
  3. On the psychology of prediction.
    Kahneman, Daniel, Tversky, Amos · 1973 · Psychological Review
  4. Handbook of research on mathematics teaching and learning
    1992 · Choice Reviews Online
  5. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
    David Marr · 1982 · Cumulative Index of Computer Aided Architectural Design
  6. The Adapted mind : evolutionary psychology and the generation of culture
    Jerome H. Barkow, Leda Cosmides, John Tooby · 1992
  7. Subjective probability: A judgment of representativeness
    Kahneman, Daniel, Tversky, Amos · 1972 · Cognitive Psychology
  8. Essentials of Behavioral Research: Methods and Data Analysis
    Robert Rosenthal, Ralph L. Rosnow · 1984
  9. Decision analysis and behavioral research
    Detlof von Winterfeldt, Ward Edwards · 1986 · Medical Entomology and Zoology
  10. How to improve Bayesian reasoning without instruction: Frequency formats.
    Gerd Gigerenzer, Ulrich Hoffrage · 1995 · Psychological Review

Show all 66 references

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    Lieder, Falk, Griffiths, Thomas L. · 2020 · Behavioral and Brain Sciences
  4. Language Acquisition as Rational Contingency Learning
    Ellis, Nick C. · 2006 · Applied Linguistics
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    Douglas, Karen M., Sutton, Robbie M., Callan, Mitchell J., Dawtry, Rael J., Harvey, Annelie J. · 2016 · Thinking & Reasoning

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Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Teaching Bayesian reasoning in less than two hours. https://app.cochat.ai/papers/10.1037//0096-3445.130.3.380
The paper
Sedlmeier, P., & Gigerenzer, G. (2001). Teaching Bayesian reasoning in less than two hours. Journal of Experimental Psychology: General. https://doi.org/10.1037//0096-3445.130.3.380

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