In recent years, there has been a growing recognition that P values, albeit useful in reporting data analysis results, have often been misused or misinterpreted in biomedical research. The emergence of big health data such as genomics data and electronic health records, sometimes combined with inadequate experimental design, has exacerbated this problem, which has become a major cause of the ongoing crisis in reproducibility in biomedical research. We aim to shed light and raise awareness of common misuses and pitfalls of P values and discuss potential mitigation strategies that leverage state-of-the-art statistical methods. The best practices always start with a sound study design including a robust data collection strategy to minimize data bias and a carefully thought-out analysis plan that can address potential misuses and pitfalls of P values. We highly encourage biomedical researchers to engage and involve statisticians from the very beginning of their studies.

中文翻译：

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解决生物医学研究中 P 值的常见误用和陷阱

近年来，人们越来越认识到，P 值虽然在报告数据分析结果方面很有用，但在生物医学研究中经常被误用或误解。基因组数据和电子健康记录等健康大数据的出现，有时再加上不充分的实验设计，加剧了这一问题，这已成为生物医学研究中持续存在的可重复性危机的主要原因。我们的目标是阐明 P 值的常见误用和陷阱并提高人们的认识，并讨论利用最先进的统计方法的潜在缓解策略。最佳实践始终从健全的研究设计开始，包括稳健的数据收集策略，以最大限度地减少数据偏差，以及经过深思熟虑的分析计划，以解决 P 值的潜在误用和陷阱。