Harnessing generative artificial intelligence for teaching statistics in medical research: Strategies for accurate hypothesis testing

Generative artificial intelligence (AI) is reshaping the way statistics is taught and applied in medical research. This paper explores the use of AI tools, with a focus on Julius AI, to support hypothesis testing and statistical reasoning among medical students and researchers. Using a practical example and comparative analysis of prompting strategies, we demonstrate how AI can assist in performing t ‐tests and interpreting results. However, we also highlight the risks of misapplication and the importance of validating AI outputs. A structured framework is proposed to guide educators in integrating AI into the curriculum, emphasizing assumption testing, stepwise analysis, and critical evaluation. By embedding AI within a pedagogically sound approach, this work aims to enhance statistical literacy, promote responsible use of technology, and improve the quality of data analysis in medical education.