— Kevin B Korb†
Sally Clark, in an infamous miscarriage of justice, was convicted of murdering her two sons in the UK in 1999 after a prosecution which employed primarily statistical reasoning in a way that has become notorious as the "prosecutor's fallacy". Here I will briefly review the arguments and the statistical reasoning from a Bayesian perspective. I don't propose the details of this analysis (i.e., the exact probabilities) be taken too seriously. They are taken from fairly cursory searches on the Internet and applied in a fairly crude way. Regardless, they are far more serious than anything produced during the trial itself!
Sally Clark was arrested after her second baby died a few months old, apparently of sudden infant death syndrome (SIDS), exactly as her first child had died a year earlier. According to prosecution testimony (by a pediatrician, Sir Roy Meadow), about 1 in 8543 babies die of SIDS. They argued that there is only a probability of that two such deaths would happen in the same family by chance alone (after controlling for tobacco smoke and a few social factors). According to the prosecution, the woman was guilty beyond a reasonable doubt. The jury returned a guilty verdict, even though there was no substantial evidence of guilt presented beyond this argument.
Let h = Clark is guilty, e1 = the evidence of the first son's death, e2 = the evidence of the second son's death. Note that the latter two are meant to establish the appearance of SIDS deaths. Then the prosecutor's argument was:
There are a lot of problems with this argument. Here I will discuss the two most basic errors, which probably have the most impact and which anyone involved with assessing evidence should be capable of recognizing. First, the combination of the evidence in (2), simply by multiplication, requires the two pieces of evidence to be independent of each other. The general form of such a combination is , which further reduces to (2) only if , that is, only if the two items of evidence are independent given innocence. However, risk factors for SIDS are very likely to be common to multiple children within a family, including not the just tobacco smoke and the social factors controlled for, but also poor prenatal care, low birth weights, alcohol consumption and sleeping practices (and, to be sure, physical abuse by parents). In any case, one SIDS death is well known to raise the probability of another in the family;‡ therefore, the combined evidence of two deaths must have a higher probability than their simple multiplication. One study reported a relative risk of recurrence of SIDS of 5 times the background rate, a rate found to be comparable to other recurrent mortality risks in siblings. This yields , instead of .
The second failure in the prosecution argument is the complete neglect of prior probabilities. Bayes' rule says:
Contrary to a widespread view in the legal community that statistical, and especially Bayesian, reasoning should not be considered in court proceedings, it is crucial in many cases that such reasoning be used — but, of course, used correctly. Many people find correct statistical reasoning difficult, but there are ways and means of improving it, some of which we will discuss in this blog. Meanwhile, if you are interested in Bayes and the Law, you might want to take a look at Norman Fenton's project.
† I thank Professor Philip Dawid for bringing this case to my attention and for helpful comments on it. His testimony to the appellate court on this case can be read here.
‡ This is so despite the widespread counselling of parents to the contrary and claims by various studies indicating no increased risk to siblings of SIDS victims! These studies all take pains to control for the kinds of risk factors I've identified above. What is relevant here is the increased risk of SIDS regardless of the cause (excepting those that Meadows actually did control for), and so the risk without controlling for alcohol, etc. is what is of interest. That risk, of course, is increased by the occurrence of a SIDS case in the family (observing an effect of a cause raises the probability of another effect being present!). The contrary claim, by the way, is probably put to parents as a means of reassurance; however, it could easily lead to complacency and to a neglect to deal with the risk factors in place in a family — in other words, made without qualification, the advice is both wrong and irresponsible.
Ray Hill has worked on this exact problem, and also related legal problems, with a few publications:
“Cot death or murder – weighing the probabilities”, Developmental Physiology Conference, June 2002.
“Multiple sudden infant deaths – coincidence or beyond coincidence?”, Paediatric and Perinatal Epidemiology, 18 (2004), 320-326.
“Reflections on the cot death cases”, Significance, 2 (2005), 13-15.
His website includes the details and copies of the above papers for downloading:
I have used it as an example in my introductory text on Bayesian methods for ecologists.
Yes, it's a good example. Thanks for the refs!
We also used this as an example in our textbook Bayesian Artificial Intelligence back in 2003, but only as a set problem. Not only has Kevin now provided a solution, but clearly an enterprising student could have found one in Hill's paper!
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