The persistent issue of wrongful convictions in the United States emphasizes the need for scrutiny and improvement of the criminal justice system. While statistical methods for the evaluation of forensic evidence, including glass, fingerprints, and DNA, have significantly contributed to solving intricate crimes, there is a notable lack of national-level standards to ensure the appropriate application of statistics in forensic investigations. We discuss the obstacles in the application of statistics in court, and emphasize the importance of making statistical interpretation accessible to non-statisticians, especially those who make decisions about potentially innocent individuals. We investigate the use and misuse of statistical methods in crime investigations, in particular the likelihood ratio approach. We further describe the use of graphical models, where hypotheses and evidence can be represented as nodes connected by arrows signifying association or causality. We emphasize the advantages of special graph structures, such as object-oriented Bayesian networks and chain event graphs, which allow for the concurrent examination of evidence of various nature.

翻译：美国持续存在的冤假错案问题凸显了对刑事司法系统进行审查与改进的紧迫性。尽管用于评估法医学证据（包括玻璃、指纹和DNA）的统计方法在破解复杂案件中发挥了重要作用，但国家层面仍缺乏确保统计学在法医调查中恰当应用的标准。我们探讨了统计学在法庭应用中的障碍，并强调向非统计学者（尤其是可能决定无辜者命运的人）普及统计解释的重要性。我们研究了犯罪调查中统计方法的使用与误用，特别是似然比方法。此外，我们描述了图形模型的应用，其中假设与证据可表示为通过箭头（代表关联或因果关系）连接的节点。我们重点阐述了特殊图结构（如面向对象的贝叶斯网络和链事件图）的优势，这些结构能够同步审查不同性质的证据。