Prompt Enginneering for Accurate Statistical Reasoning with Large Language Models in Medical Research

The integration of generative artificial intelligence (AI), particularly large language models (LLMs), into medical statistics presents both significant opportunities and critical risks. This paper explores how prompt engineering, defined as the deliberate design of inputs to guide AI behavior, can help mitigate statistical errors in biomedical research. Four prompting strategies are evaluated: zero-shot, explicit instruction, chain of thought, and hybrid approaches. Case studies involving descriptive and inferential statistical tasks show that while zero-shot prompting is generally sufficient for basic summaries, more complex analyses require structured, multi-step prompts to ensure methodological soundness. Among the strategies assessed, hybrid prompting, which combines explicit instructions, reasoning scaffolds, and output formatting, consistently produced the most accurate and interpretable results across two LLMs. The findings emphasize that prompt design, rather than model architecture alone, is the primary determinant of output quality. Although limited access to a broader range of models is a constraint, this study highlights the importance of prompt engineering as a core competency in AI-assisted medical research. It calls for the development of standardized prompt templates, evaluation rubrics, and further studies across diverse statistical domains to support robust and reproducible scientific inquiry.