PDF(Pubmed)
Abstract：
Overconfidence in statistical results in medicine is fueled by improper practices and historical biases afflicting the concept of statistical significance. In particular, the dichotomization of significance (i.e., significant vs. not significant), blending of Fisherian and Neyman-Pearson approaches, magnitude and nullification fallacies, and other fundamental misunderstandings distort the purpose of statistical investigations entirely, impacting their ability to inform public health decisions or other fields of science in general. For these reasons, the international statistical community has attempted to propose various alternatives or different interpretative modes. However, as of today, such misuses still prevail. In this regard, the present paper discusses the use of multiple confidence (or, more aptly, compatibility) intervals to address these issues at their core. Additionally, an extension of the concept of confidence interval, called surprisal interval (S-interval), is proposed in the realm of statistical surprisal. The aforementioned is based on comparing the statistical surprise to an easily interpretable phenomenon, such as obtaining S consecutive heads when flipping a fair coin. This allows for a complete departure from the notions of statistical significance and confidence, which carry with them longstanding misconceptions.
摘要：
对医学统计结果的过度自信是由不适当的做法和历史偏见助长的统计意义的概念。特别是,意义的二分法(即，显著vs.不重要)，渔民和内曼-皮尔森方法的混合，规模和无效谬论，和其他基本误解完全扭曲了统计调查的目的，影响他们为公共卫生决策或其他一般科学领域提供信息的能力。由于这些原因,国际统计界试图提出各种替代方案或不同的解释模式。然而,截至今天,这种滥用仍然普遍存在。在这方面,本文讨论了多重置信度的使用(或，更恰当地,兼容性)以解决这些问题为核心的间隔。此外,置信区间概念的扩展，称为令人惊讶的间隔（S间隔），是在统计惊喜领域提出的。上述是基于将统计惊喜与易于解释的现象进行比较，例如在抛一枚公平的硬币时获得S个连续的头。这允许完全背离统计显著性和置信度的概念，它们带有长期的误解。
