IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i9p1558-d809007.html
   My bibliography  Save this article

How to Train Novices in Bayesian Reasoning

Author

Listed:
  • Theresa Büchter

    (Institute of Mathematics, University of Kassel, 34132 Kassel, Germany)

  • Andreas Eichler

    (Institute of Mathematics, University of Kassel, 34132 Kassel, Germany)

  • Nicole Steib

    (Faculty of Mathematics, University of Regensburg, 93053 Regensburg, Germany)

  • Karin Binder

    (Institute of Mathematics, Ludwig-Maximilians-University Munich, 80333 München, Germany)

  • Katharina Böcherer-Linder

    (Department of Mathematics Education, University of Freiburg, 79104 Freiburg, Germany)

  • Stefan Krauss

    (Faculty of Mathematics, University of Regensburg, 93053 Regensburg, Germany)

  • Markus Vogel

    (Institute of Mathematics, University of Education Heidelberg, 69120 Heidelberg, Germany)

Abstract

Bayesian Reasoning is both a fundamental idea of probability and a key model in applied sciences for evaluating situations of uncertainty. Bayesian Reasoning may be defined as the dealing with, and understanding of, Bayesian situations. This includes various aspects such as calculating a conditional probability ( performance ), assessing the effects of changes to the parameters of a formula on the result ( covariation ) and adequately interpreting and explaining the results of a formula ( communication ). Bayesian Reasoning is crucial in several non-mathematical disciplines such as medicine and law. However, even experts from these domains struggle to reason in a Bayesian manner. Therefore, it is desirable to develop a training course for this specific audience regarding the different aspects of Bayesian Reasoning. In this paper, we present an evidence-based development of such training courses by considering relevant prior research on successful strategies for Bayesian Reasoning (e.g., natural frequencies and adequate visualizations) and on the 4C/ID model as a promising instructional approach. The results of a formative evaluation are described, which show that students from the target audience (i.e., medicine or law) increased their Bayesian Reasoning skills and found taking part in the training courses to be relevant and fruitful for their professional expertise.

Suggested Citation

  • Theresa Büchter & Andreas Eichler & Nicole Steib & Karin Binder & Katharina Böcherer-Linder & Stefan Krauss & Markus Vogel, 2022. "How to Train Novices in Bayesian Reasoning," Mathematics, MDPI, vol. 10(9), pages 1-31, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1558-:d:809007
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/9/1558/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/9/1558/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sloman, Steven A. & Over, David & Slovak, Lila & Stibel, Jeffrey M., 2003. "Frequency illusions and other fallacies," Organizational Behavior and Human Decision Processes, Elsevier, vol. 91(2), pages 296-309, July.
    2. C. J. Wild & M. Pfannkuch, 1999. "Statistical Thinking in Empirical Enquiry," International Statistical Review, International Statistical Institute, vol. 67(3), pages 223-248, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maria Penna & Mirian Agus & Maribel Peró-Cebollero & Joan Guàrdia-Olmos & Eliano Pessa, 2014. "The use of imagery in statistical reasoning by university undergraduate students: a preliminary study," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(1), pages 173-187, January.
    2. Joel B. Greenhouse & Howard J. Seltman, 2018. "On Teaching Statistical Practice: From Novice to Expert," The American Statistician, Taylor & Francis Journals, vol. 72(2), pages 147-154, April.
    3. Richard B. Anderson & Laura Marie Leventhal & Don C. Zhang & Daniel Fasko, Jr. & Zachariah Basehore & Christopher Gamsby & Jared Branch & Timothy Patrick, 2019. "Belief bias and representation in assessing the Bayesian rationality of others," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(1), pages 1-10, January.
    4. Claudia Vásquez & Israel García-Alonso & María José Seckel & Ángel Alsina, 2021. "Education for Sustainable Development in Primary Education Textbooks—An Educational Approach from Statistical and Probabilistic Literacy," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    5. repec:cup:judgdm:v:3:y:2008:i::p:140-152 is not listed on IDEAS
    6. Claudia Vásquez & Ángel Alsina, 2021. "Analysing Probability Teaching Practices in Primary Education: What Tasks Do Teachers Implement?," Mathematics, MDPI, vol. 9(19), pages 1-21, October.
    7. Yuichi Amitani, 2015. "The natural frequency hypothesis and evolutionary arguments," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 14(1), pages 1-19, June.
    8. Christoph Engel & Andreas Glöckner, 2008. "Can We Trust Intuitive Jurors? An Experimental Analysis," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2008_36, Max Planck Institute for Research on Collective Goods.
    9. Laura Muñiz-Rodríguez & Luis J. Rodríguez-Muñiz & Ángel Alsina, 2020. "Deficits in the Statistical and Probabilistic Literacy of Citizens: Effects in a World in Crisis," Mathematics, MDPI, vol. 8(11), pages 1-20, October.
    10. Jenna Hicks & Jessica Dewey & Yaniv Brandvain & Anita Schuchardt, 2020. "Development of the Biological Variation In Experimental Design And Analysis (BioVEDA) assessment," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-21, July.
    11. Seokmin Kang & Sungyeun Kim, 2022. "Lessons Learned from Topic Modeling Analysis of COVID-19 News to Enrich Statistics Education in Korea," Sustainability, MDPI, vol. 14(6), pages 1-16, March.
    12. Freling, Traci H. & Yang, Zhiyong & Saini, Ritesh & Itani, Omar S. & Rashad Abualsamh, Ryan, 2020. "When poignant stories outweigh cold hard facts: A meta-analysis of the anecdotal bias," Organizational Behavior and Human Decision Processes, Elsevier, vol. 160(C), pages 51-67.
    13. Shira Elqayam & David Over, 2012. "Probabilities, beliefs, and dual processing: the paradigm shift in the psychology of reasoning," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 11(1), pages 27-40, June.
    14. Zhiyong Yang & Ritesh Saini & Traci Freling, 2015. "How Anxiety Leads to Suboptimal Decisions Under Risky Choice Situations," Risk Analysis, John Wiley & Sons, vol. 35(10), pages 1789-1800, October.
    15. Jeffrey Stibel & Itiel Dror & Talia Ben-Zeev, 2009. "The Collapsing Choice Theory: Dissociating Choice and Judgment in Decision Making," Theory and Decision, Springer, vol. 66(2), pages 149-179, February.
    16. Săvoiu, Gheorghe, 2008. "The scientifiv way of thinking in statistics, statistical physics and quantum mechanics," MPRA Paper 13558, University Library of Munich, Germany.
    17. Patricia Belén Carrera & Luis R. Pino-Fan & Hugo Alvarado & Jesús Guadalupe Lugo-Armenta, 2021. "Practices of the Random Variable Proposed in the Chilean Mathematics Curriculum of Secondary Education," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    18. Christine Ohlert & Barbara Weißenberger, 2015. "Beating the base-rate fallacy: an experimental approach on the effectiveness of different information presentation formats," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 26(1), pages 51-80, April.
    19. Ricardo Ocaña-Riola, 2016. "The Use of Statistics in Health Sciences: Situation Analysis and Perspective," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 204-219, October.
    20. Jesús Guadalupe Lugo-Armenta & Luis Roberto Pino-Fan, 2021. "Inferential Reasoning of Secondary School Mathematics Teachers on the Chi-Square Statistic," Mathematics, MDPI, vol. 9(19), pages 1-20, September.
    21. repec:cup:judgdm:v:1:y:2006:i::p:108-117 is not listed on IDEAS
    22. repec:cup:judgdm:v:9:y:2014:i:3:p:226-242 is not listed on IDEAS
    23. Mary Kynn, 2008. "The ‘heuristics and biases’ bias in expert elicitation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 239-264, January.

    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:gam:jmathe:v:10:y:2022:i:9:p:1558-:d:809007. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.