  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.
