Harnessing AI for Teaching Statistics in Medical Research: Strategies for Accurate Hypothesis Testing

The integration of generative AI into statistical analysis is revolutionizing medical research, offering unprecedented opportunities for enhancing data interpretation and educational methodologies. This paper explores the challenges and potential misuse of AI-generated outputs in statistical data analysis, specifically focusing on t-tests. Through a detailed examination, we highlight the pitfalls associated with over-reliance on AI and propose a robust framework for using AI tools like Julius AI in hypothesis testing. Our findings demonstrate that while AI can significantly aid in understanding and applying statistical tests, it is essential to use these tools correctly to avoid erroneous outputs and misinterpretations. By adopting a step-by-step approach, educators can empower medical students and researchers to leverage AI effectively, thereby improving their analytical skills and critical thinking. This framework not only enhances the learning experience but also ensures accurate and reliable data analysis, ultimately contributing to more robust and valid conclusions in medical research.