TL11: Using JASP to Teach Bayesian Statistics to Health Science Students

Music City Center 

Bayesian statistics offers a robust framework for addressing uncertainty in data analysis, yet it remains underutilized in health science education. This talk explores the integration of Bayesian statistics into health science curricula through the use of JASP (Jeffreys's Amazing Statistics Program), a user-friendly, open-source software platform designed for statistical analysis. By leveraging JASP's intuitive interface and visualizations, students can transition seamlessly from traditional frequentist methods to Bayesian approaches without extensive programming experience.

The presentation will outline a teaching strategy that combines theoretical instruction with hands-on data analysis, focusing on health science case studies relevant to students' future careers. Key topics include Bayesian parameter estimation, hypothesis testing, and credible intervals. We will discuss the benefits of Bayesian thinking in clinical decision-making, particularly in areas such as diagnostic testing, treatment efficacy, and evidence synthesis.

Illustrative examples will showcase how JASP facilitates interactive learning by allowing students to manipulate prior and posterior distributions.

Bayesian Statistics, Health Science Education, JASP Software, Evidence-Based Decision-Making 

Section on Teaching of Statistics in the Health Sciences