For several decades, legal and scientific scholars have argued that conclusions from forensic examinations should be supported by statistical data and reported within a probabilistic framework. Multiple models have been proposed to quantify and express the probative value of forensic evidence. Unfortunately, the use of statistics to perform inferences in forensic science adds a layer of complexity that most forensic scientists, court officers and lay individuals are not armed to handle. Many applications of statistics to forensic science rely on ad-hoc strategies and are not scientifically sound. The opacity of the technical jargon used to describe probabilistic models and their results, and the complexity of the techniques involved make it very difficult for the untrained user to separate the wheat from the chaff. This series of papers is intended to help forensic scientists and lawyers recognize limitations and issues in tools proposed to interpret the results of forensic examinations. This article focuses on tools that have been proposed to leverage the use of similarity scores to assess the probative value of forensic findings. We call this family of tools ‘score-based likelihood ratios’. In this article, we present the fundamental concepts on which these tools are built, we describe some specific members of this family of tools, and we compare them explore to the Bayes factor through an intuitive geometrical approach and through simulations. Finally, we discuss their validation and their potential usefulness as a decision-making tool in forensic science.

中文翻译：

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抵御现代艺术：统计的诅咒-第二部分：“基于得分的似然比”

几十年来，法律和科学学者一直认为，法医检查的结论应得到统计数据的支持，并应在概率框架内进行报告。已经提出了多种模型来量化和表达法医证据的证明价值。不幸的是，使用统计数据进行法医科学推理会增加一层复杂性，大多数法医科学家，法院官员和非专业人士没有武装要处理。统计学在法医学上的许多应用都依赖于即席策略，而且在科学上也不合理。用于描述概率模型及其结果的技术术语的不透明性以及所涉及技术的复杂性使得未经培训的用户很难将小麦与谷壳分离。本系列文章旨在帮助法医科学家和律师认识到建议用来解释法医检查结果的工具中的局限性和问题。本文重点介绍已提出的工具，这些工具可以利用相似性评分来评估法医调查结果的证明价值。我们称这类工具为“基于得分的似然比”。在本文中，我们介绍了构建这些工具的基本概念，描述了该工具系列的一些特定成员，并通过直观的几何方法和模拟将它们与贝叶斯因子进行了比较。最后，我们讨论了它们的验证及其作为法医科学决策工具的潜在用途。