Statistical analyses form a fundamental part of causal inference in the experimental sciences. The statistical paradigm most commonly taught to science students around the world is that of frequentism, with a particular emphasis on the null hypothesis significance testing borne by the work of Neyman and Pearson in the early 20th century. This paradigm is often lauded as being the most objective of methods and remains commonplace in scientific journals. Despite its widespread use-and, indeed, requirement for publication in some journals-this paradigm has received substantial criticism in recent decades, and its impact on scientific publishing has been subjected to deeper scrutiny in response to the replication crisis in the psychological and medical sciences. It has been posited that the increasing use of the Bayesian statistical paradigm, made more accessible through technological advances in the last few decades, may have an important role to play in rendering research and statistical inference more robust, transparent, and reproducible. These methods can have a steep learning curve, and thus this paper seeks to introduce those working within clinical laboratories to the Bayesian paradigm of statistical analysis and provides worked examples of the Bayesian analysis of data commonly encountered in laboratory medicine using freely available, open source tools.

中文翻译：

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检验医学中贝叶斯统计实用指南。

统计分析是实验科学中因果推理的基本组成部分。世界各地最常教授给理科学生的统计范式是频率论，特别强调 20 世纪初 Neyman 和 Pearson 的工作所承担的零假设显着性检验。这种范式经常被称赞为最客观的方法，并且在科学期刊中仍然司空见惯。尽管它被广泛使用——事实上，在某些期刊上发表的要求——这种范式在近几十年来受到了广泛的批评，并且它对科学出版的影响已经受到更深入的审查，以应对心理和医学科学的复制危机. 假设贝叶斯统计范式的使用越来越多，通过过去几十年的技术进步更容易获得，可能在使研究和统计推断更加稳健、透明和可重复方面发挥重要作用。这些方法可能有一个陡峭的学习曲线，因此本文试图将那些在临床实验室工作的人介绍贝叶斯统计分析范式，并提供使用免费提供的开源工具对实验室医学中常见的数据进行贝叶斯分析的工作示例.