Fenton Norman, Berger Daniel, Lagnado David, Neil Martin, Hsu Anne
Queen Mary University of London, United Kingdom.
Queen Mary University of London, United Kingdom.
Sci Justice. 2014 Jul;54(4):274-87. doi: 10.1016/j.scijus.2013.07.002. Epub 2013 Aug 16.
The likelihood ratio (LR) is a probabilistic method that has been championed as a 'simple rule' for evaluating the probative value of forensic evidence in court. Intuitively, if the LR is greater than one then the evidence supports the prosecution hypothesis; if the LR is less than one it supports the defence hypothesis, and if the LR is equal to one then the evidence favours neither (and so is considered 'neutral'-having no probative value). It can be shown by Bayes' theorem that this simple relationship only applies to pairs of hypotheses for which one is the negation of the other (i.e. to mutually exclusive and exhaustive hypotheses) and is not applicable otherwise. We show how easy it can be - even for evidence experts - to use pairs of hypotheses that they assume are mutually exclusive and exhaustive but are not, and hence to arrive at erroneous conclusions about the value of evidence using the LR. Furthermore, even when mutually exclusive and exhaustive hypotheses are used there are extreme restrictions as to what can be concluded about the probative value of evidence just from a LR. Most importantly, while the distinction between source-level hypotheses (such as defendant was/was not at the crime scene) and offence-level hypotheses (defendant is/is not guilty) is well known, it is not widely understood that a LR for evidence about the former generally has no bearing on the LR of the latter. We show for the first time (using Bayesian networks) the full impact of this problem, and conclude that it is only the LR of the offence level hypotheses that genuinely determines the probative value of the evidence. We investigate common scenarios in which evidence has a LR of one but still has significant probative value (i.e. is not neutral as is commonly assumed). As illustration we consider the ramifications of these points for the case of Barry George. The successful appeal against his conviction for the murder of Jill Dando was based primarily on the argument that the firearm discharge residue (FDR) evidence, assumed to support the prosecution hypothesis at the original trial, actually had a LR equal to one and hence was 'neutral'. However, our review of the appeal transcript shows numerous examples of the problems with the use of hypotheses identified above. We show that if one were to follow the arguments recorded in the Appeal judgement verbatim, then contrary to the Appeal conclusion, the probative value of the FDR evidence may not have been neutral as was concluded.
似然比（LR）是一种概率方法，一直被推崇为评估法庭上法医证据证明力的“简单规则”。直观地说，如果似然比大于1，那么该证据支持控方假设；如果似然比小于1，则支持辩方假设；如果似然比等于1，那么该证据对双方均无偏向（因此被视为“中立”——没有证明力）。根据贝叶斯定理可以证明，这种简单关系仅适用于其中一个假设是另一个假设的否定的假设对（即互斥且完备的假设），否则不适用。我们展示了即使对于证据专家来说，使用他们认为互斥且完备但实际并非如此的假设对是多么容易，从而导致使用似然比对证据价值得出错误结论。此外，即使使用了互斥且完备的假设，仅从似然比得出关于证据证明力的结论也有极大限制。最重要的是，虽然源层面假设（如被告是否在犯罪现场）和罪行层面假设（被告是否有罪）之间的区别广为人知，但人们普遍没有理解关于前者的证据的似然比通常与后者的似然比无关。我们首次（使用贝叶斯网络）展示了这个问题的全面影响，并得出结论，真正决定证据证明力的是罪行层面假设的似然比。我们研究了证据的似然比为1但仍具有重大证明力（即不像通常假设的那样是中立的）的常见情形。作为例证，我们考虑了这些观点对巴里·乔治案的影响。他因谋杀吉尔·丹多被定罪，但成功上诉，主要依据的论点是，在原审中被认为支持控方假设的枪支射击残留物（FDR）证据，实际上似然比等于1，因此是“中立的”。然而，我们对上诉记录的审查显示了上述假设使用问题的众多例子。我们表明，如果逐字遵循上诉判决中记录的论点，那么与上诉结论相反，FDR证据的证明力可能并非如结论中所说的那样是中立的。
