IDEAS home Printed from https://ideas.repec.org/a/taf/amstat/v73y2019i2p186-190.html
   My bibliography  Save this article

Teaching Bayes’ Theorem: Strength of Evidence as Predictive Accuracy

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00031305.2017.1341334
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00031305.2017.1341334?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:amstat:v:73:y:2019:i:2:p:186-190. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UTAS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.