正如文献和美国统计协会等国际机构广泛指出的那样,对P值的严重误解,置信区间,和统计意义在公共卫生中很常见。这种情况会带来有关最终决定的严重风险,例如批准或拒绝治疗。对统计数据的认知扭曲可能源于学校和大学的糟糕教学,过于简化的解释,正如我们所建议的那样,不计后果地使用具有预定义标准化程序的计算软件。鉴于此,我们提出了一个框架来重新校准频繁推理统计在临床和流行病学研究中的作用。特别是,我们强调,统计数据只是一组规则和数字,只有在事先适当地置于明确定义的科学背景下才有意义。出于教育目的讨论了实际例子。除此之外,我们提出了一些工具来更好地评估统计结果,例如多个兼容性或令人惊讶的间隔或各种点假设的元组。最后,我们强调,每个结论都必须由不同类型的科学证据(例如,生物化学,临床,统计,等。),并且必须基于对成本的仔细检查,风险,和好处。
As widely noted in the literature and by international bodies such as the American Statistical Association, severe misinterpretations of P-values, confidence intervals, and statistical significance are sadly common in public health. This scenario poses serious risks concerning terminal decisions such as the approval or rejection of therapies. Cognitive distortions about statistics likely stem from poor teaching in schools and universities, overly simplified interpretations, and - as we suggest - the reckless use of calculation software with predefined standardized procedures. In light of this, we present a framework to recalibrate the role of frequentist-inferential statistics within clinical and epidemiological research. In particular, we stress that statistics is only a set of rules and numbers that make sense only when properly placed within a well-defined scientific context beforehand. Practical examples are discussed for educational purposes. Alongside this, we propose some tools to better evaluate statistical outcomes, such as multiple compatibility or surprisal intervals or tuples of various point hypotheses. Lastly, we emphasize that every conclusion must be informed by different kinds of scientific evidence (e.g., biochemical, clinical, statistical, etc.) and must be based on a careful examination of costs, risks, and benefits.