Section on Teaching of Statistics in the Health Sciences P.M. Roundtable Discussion (Added Fee)

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. 

This roundtable discussion will focus on the role of statisticians in academic nursing, emphasizing both education and research collaboration. Nursing education requires strong statistical training to support evidence-based practice, while nursing research depends on advanced statistical methods such as psychometrics, clinical trials, and health outcomes analysis. Discussion topics will include curriculum development, effective pedagogical strategies, and assessment methods to enhance statistical literacy among graduate nursing students. Key nursing research applications, including statistical modeling and measurement, will be discussed, along with strategies for addressing common statistical challenges in nursing science. This session aims to provide statisticians and nursing educators with insights into improving statistical education and fostering interdisciplinary research collaboration.