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Teaching Bayes’ Theorem: Strength of Evidence as Predictive Accuracy

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  • Jeffrey N. Rouder
  • Richard D. Morey

Abstract

Although teaching Bayes’ theorem is popular, the standard approach—targeting posterior distributions of parameters—may be improved. We advocate teaching Bayes’ theorem in a ratio form where the posterior beliefs relative to the prior beliefs equals the conditional probability of data relative to the marginal probability of data. This form leads to an interpretation that the strength of evidence is relative predictive accuracy. With this approach, students are encouraged to view Bayes’ theorem as an updating mechanism, to obtain a deeper appreciation of the role of the prior and of marginal data, and to view estimation and model comparison from a unified perspective.

Suggested Citation

  • Jeffrey N. Rouder & Richard D. Morey, 2019. "Teaching Bayes’ Theorem: Strength of Evidence as Predictive Accuracy," The American Statistician, Taylor & Francis Journals, vol. 73(2), pages 186-190, April.
    • Handle: RePEc:taf:amstat:v:73:y:2019:i:2:p:186-190
    DOI: 10.1080/00031305.2017.1341334
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    Cited by:

    1. David Wheatley & Tiffany Bayley & Mojtaba Araghi, 2022. "Able Construction: A Spreadsheet Activity for Teaching Bayes’ Theorem," SN Operations Research Forum, Springer, vol. 3(1), pages 1-18, March.
    2. Patrícia Martinková & František Bartoš & Marek Brabec, 2023. "Assessing Inter-rater Reliability With Heterogeneous Variance Components Models: Flexible Approach Accounting for Contextual Variables," Journal of Educational and Behavioral Statistics, , vol. 48(3), pages 349-383, June.
    3. Bojana Većkalov & Sandra J. Geiger & František Bartoš & Mathew P. White & Bastiaan T. Rutjens & Frenk Harreveld & Federica Stablum & Berkan Akın & Alaa Aldoh & Jinhao Bai & Frida Berglund & Aleša Brat, 2024. "A 27-country test of communicating the scientific consensus on climate change," Nature Human Behaviour, Nature, vol. 8(10), pages 1892-1905, October.

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