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中国临床药理学与治疗学 ›› 2026, Vol. 31 ›› Issue (2): 240-246.doi: 10.12092/j.issn.1009-2501.2026.02.011

• 定量药理学 • 上一篇    下一篇

P值应用思考:挑战与优化策略

刘志伟1,2(), 陈舒晴3(), 陈右君1,2, 李一鸣1,2, 言方荣4, 赵杨5, 孙华1, 谢海棠1,*(), 王陵6,*()   

  1. 1. 皖南医学院弋矶山医院药物临床评价中心,芜湖 241001,安徽
    2. 皖南医学院,芜湖 241002,安徽
    3. 华中科技大学同济医学院,公共卫生学院,流行病学与生物统计学系,武汉 430030,湖北
    4. 中国药科大学理学院,南京 211198,江苏
    5. 南京医科大学公共卫生学院生物统计学系,南京 211166,江苏
    6. 空军军医大学军事预防医学系卫生统计学教研室,西安 710032,陕西
  • 收稿日期:2025-08-24 修回日期:2026-02-06 出版日期:2026-02-26 发布日期:2026-03-17
  • 通讯作者: 谢海棠,王陵 E-mail:974973701@qq.com;632227690@qq.com;xiehaitang2023@163.com;lynnw@fmmu.edu.cn
  • 作者简介:刘志伟,男,硕士研究生,研究方向:定量药理学。 E-mail:974973701@qq.com|陈舒晴,共同第一作者,女,博士研究生,研究方向:临床药学和统计学。E-mail:632227690@qq.com
  • 基金资助:
    2023年安徽省临床医学研究转化专项(202304295107020005)

Current application of P-value: challenges and optimization strategies

Zhiwei LIU1,2(), Shuqing CHEN3(), Youjun CHEN1,2, Yiming LI1,2, Fangrong YAN4, Yang ZHAO5, Hua SUN1, Haitang XIE1,*(), Ling WANG6,*()   

  1. 1. Yijishan Hospital Clinical Trial Center, Wannan Medical College, Wuhu 241001, Anhui, China
    2. Wannan Medical College, Wuhu 241002, Anhui, China
    3. Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, China
    4. School of Science, China Pharmaceutical University, Nanjing 211198, Jiangsu, China
    5. Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
    6. Department of Health Statistics, Faculty of Military Preventive Medicine, Air Force Medical University, Xi’an 710032, Shaanxi, China
  • Received:2025-08-24 Revised:2026-02-06 Online:2026-02-26 Published:2026-03-17
  • Contact: Haitang XIE,Ling WANG E-mail:974973701@qq.com;632227690@qq.com;xiehaitang2023@163.com;lynnw@fmmu.edu.cn

摘要:

P值(基于频率学派)是统计学中用于检验原假设的重要工具,表示在原假设为真时观察到当前或更极端结果的概率。尽管P值被广泛用于判断研究结果的显著性(如P<0.05,P<0.01),但其滥用和误用也引发了诸多争议。本文综述了P值在科学研究与临床试验中的重要性及局限性,包括其对样本量的敏感性、二元分类的误导性,以及无法客观反映有无临床意义等问题。同时,探讨了作为补充和优化的方法,如置信区间、效应量、贝叶斯统计等。基于现有共识与争议,提出合理使用P值的建议,强调结合多种统计指标以提升科学推断的严谨性和可重复性,认识到P值在控制错误率和组间比较中的核心作用。未来,提倡将P值重新定位为原假设的“兼容性”指标,并与其他方法协同应用,避免简单的二元分类。

关键词: 统计学, P值, 研究进展, 统计滥用

Abstract:

The P-value (based on the frequentist school of thought) is an important tool in statistics for testing the null hypothesis, representing the probability of observing the current or more extreme results when the null hypothesis is true. Although P-values are widely used to determine the statistical significance of research findings (e.g., P<0.05, P<0.01), their abuse and misuse have sparked numerous controversies. This article reviews the importance and limitations of P-values in scientific research and clinical trials, including their sensitivity to sample size, the misleading nature of binary classification, and their inability to objectively reflect clinical significance, among other issues. Additionally, it explores supplementary and optimized methods, such as confidence intervals, effect sizes, and Bayesian statistics. Based on existing consensus and controversies, recommendations for the rational use of P-values are proposed, emphasizing the integration of multiple statistical indicators to enhance the rigor and reproducibility of scientific inferences, while recognizing the core role of P-values in controlling error rates and making group comparisons. Looking ahead, it advocates repositioning P-values as an indicator of the "compatibility" with the null hypothesis and applying them in conjunction with other methods to avoid simplistic binary classification.

Key words: statistics, P-value, research progress, statistical abuse

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