Teaching Pack: Probability Revision and Bayes

In this teaching pack on Probability Revision and Bayes, students are introduced to the implications of imperfect information, acquire a conceptual understanding of Bayes theorem, and gain practical skills in performing probability revision. Materials include an instructor's note, videos, companion slides, a glossary, an annotated bibliography, sample exercises, and additional support.

Learning Objectives

1. Demonstrate a conceptual understanding of Bayes’ theorem and probability revision.
2. Differentiate between test characteristics (e.g., probability of positive test given disease presence) and posterior probabilities (e.g., probability of disease given a positive test).
3. Perform probability revision using a flow diagram, 2 x 2 table, Bayes’ formula and tree inversion.

Teaching Pack Materials

• Instructor's Note: Probability Revision and Bayes
• Videos, Part 1: Probability Revision and Bayes (2 videos)
• Videos, Part 2: Probability Revision and Bayes (4 videos)
• Multimedia Animation: Animated Tree Inversion
• Companion Slides to Videos, Part 1: Probability Revision and Bayes
• Companion Slides to Videos, Part 2: Probability Revision and Bayes
• Glossary: Probability Revision and Bayes
• Bibliography: Probability Revision and Bayes
• Sample Exercises: Probability Revision and Bayes
• Optional Video: Review of Conditional Probability, Khan Academy

This teaching pack was developed by Sue J. Goldie at the Center for Health Decision Science, Harvard T.H. Chan School of Public Health. The multimedia components were developed as part of a series of pilots in the CHDS Media Hub, led by Jake Waxman, where media-based pedagogy experiments contribute to new ways of thinking about short form content. More than 75 multimedia segments were created with a creative and consistent look and feel, designed to foster connection and social proximity with the instructor, while leveraging evidence-based multimedia design principles to optimize cognitive strategies for the learner.