Abstract
Whenever a litigant needs to prove that a certain result was caused in a specific way, what could be more compelling than citing the infinitesimal probability of that result emanating from an alternative natural cause? Contrary to this intuitive position, in the present article, I argue that the contention that a result was due to a certain cause should remain unaffected by statistical evidence of the extremely low probability of an alternative cause. The only scenario in which the low probability of a natural cause would be relevant to the case at hand is if it were contrasted with another piece of statistical evidence: the frequency of the criminal activity among people who are similar to the accused. However, by connecting the use of probabilistic generalisations in legal fact-finding to the issue of free will, I hold that, in Criminal Law, contrasting frequencies in this manner is objectionable—as a matter of principle—regardless of how reliable the statistical analysis is. Consequently, if the low probability of a natural cause is probative only if contrasted with another piece of statistical evidence that is objectionable, then neither piece of evidence should be admitted in criminal trials.
Highlights
When a litigant needs to prove that a certain result was caused in a specific way, what could make a stronger case than presenting the infinitesimal probability of that result emanating from an alternative cause? When Sally Clark was put on trial in England for the murder of her two babies, the prosecution called upon Professor Sir Roy Meadow, a leading British paediatrician of the time, who testified that the probability of two cases of Sudden Infant Death Syndrome (SIDS, or ‘cot death’) occurring in a family similar to Clark’s was just AMIT PUNDIK1 in 73 million.[1]
Whenever a litigant needs to prove that a certain result was caused in a specific way, what could be more compelling than citing the infinitesimal probability of that result emanating from an alternative natural cause? Contrary to this intuitive position, in the present article, I argue that the contention that a result was due to a certain cause should remain unaffected by statistical evidence of the extremely low probability of an alternative cause
The only scenario in which the low probability of a natural cause would be relevant to the case at hand is if it were contrasted with another piece of statistical evidence: the frequency of the criminal activity among people who are similar to the accused
Summary
When a litigant needs to prove that a certain result was caused in a specific way, what could make a stronger case than presenting the infinitesimal probability of that result emanating from an alternative cause? When Sally Clark was put on trial in England for the murder of her two babies, the prosecution called upon Professor Sir Roy Meadow, a leading British paediatrician of the time, who testified that the probability of two cases of Sudden Infant Death Syndrome (SIDS, or ‘cot death’) occurring in a family similar to Clark’s was just. While I share the view that the various accounts seek to justify why statistical evidence on the probability of criminal activity should not be admissible in court have been unsuccessful,[13] I do not defend this view here.[14] Instead, I seek to propose an alternative account based on a general theory that I have developed elsewhere,[15] according to which the use of generalisations in legal fact-finding is connected to the issue of free will When both parts of the argument are combined, the following conclusion emerges: if one piece of statistical evidence (on the minute frequency of an alternative cause) is not probative unless contrasted with another piece of statistical evidence (on the frequency of the alleged criminal conduct), and if the latter is inadmissible (because its use is objectionable as a matter of principle), both pieces of statistical evidence should be inadmissible. It follows that using statistical evidence on the low probability of an alternative cause is objectionable, regardless of how reliable the statistical analysis is
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