The growth of Bayesian statistics has created a pressing need for effective educational approaches. As Bayesian methods have moved from a specialized research topic to a mainstream methodology used across the sciences, the question of how to teach these methods—and to whom—has become increasingly important. The ISBA Section on Bayesian Education, Research, and Practice addresses this need by fostering innovation in Bayesian pedagogy and promoting evidence-based approaches to statistics education.
Mission
The section's mission encompasses three interconnected goals: advancing research on how people learn and understand Bayesian concepts, developing educational materials and curricula that make Bayesian methods accessible to students at all levels, and promoting best practices in the application of Bayesian methods across disciplines. By addressing all three areas, the section aims to close the gap between the theoretical power of Bayesian methods and their adoption in practice.
A growing movement in statistics education advocates teaching Bayesian methods from the very beginning of the statistics curriculum, rather than treating them as an advanced topic. Proponents argue that the Bayesian framework—with its intuitive interpretation of probability as a measure of uncertainty—is more natural for students than the frequentist approach and better prepares them for modern data analysis. The section actively supports research and experimentation in this area.
Activities
The section organizes conference sessions, workshops, and webinars focused on Bayesian education. Topics include the design of introductory and advanced Bayesian courses, the use of interactive software and visualizations in teaching, strategies for teaching Bayesian thinking to non-statisticians, and the assessment of student learning in Bayesian statistics. The section also curates and shares educational resources, including course syllabi, textbook recommendations, and software tutorials.
Challenges in Bayesian Education
Teaching Bayesian statistics presents unique challenges. Students must grapple with the concept of prior distributions and their role in inference, understand the computational machinery of MCMC and related algorithms, and develop the modeling skills needed to apply Bayesian methods effectively. The section supports research into how these challenges can be addressed through innovative pedagogy, including the use of simulation-based approaches, visual and interactive tools, and real-world case studies.
"The future of Bayesian statistics depends not just on methodological advances but on our ability to teach these methods effectively to the next generation of scientists and decision-makers."— Mine Çetinkaya-Rundel
Impact
The section's work has contributed to a broader shift in statistics education, with Bayesian methods now included in many undergraduate and graduate statistics curricula around the world. By bringing together educators, researchers, and practitioners, the section ensures that pedagogical innovation keeps pace with methodological development.