L.A. Times Statistics Expert Says L.A. Times “Mischaracterized” Crucial Statistic in DNA “Cold Hit” Article
A statistics expert cited by the L.A. Times has now publicly claimed that the paper “mischaracterized” a central statistic in a Page One article on DNA, statistics, and cold hits.
Prof. David Kaye was recently described by L.A. Times reporters Jason Felch and Maura Dolan as “an expert on science and the law at Arizona State University and former member of a national committee that studied forensic DNA.”
Prof. Kaye’s article is scheduled to appear in the journal “Law, Probability, and Risk” in September 2008. Alert readers will recognize the error cited by Prof. Kaye as one that I have repeatedly complained about on this blog. Here’s Prof. Kaye:
Diana Sylvester, a “22-year-old San Francisco nurse had been sexually assaulted and stabbed in the heart” in her San Francisco apartment over thirty years ago. A DNA database match from a highly degraded semen sample led investigators to “John Puckett, an obese, wheelchair-bound 70-year-old with a history of rape.” The jury heard that the random-match probability for the match at five or so loci was about 1 in 1.1 million. It did not learn that the California database had 338,000 profiles in it, making np almost 1 in 3 — a number that would render the match almost worthless to the prosecution (and that the reporters mischaracterized as “the probability that the database search had hit upon an innocent person”).
(All emphasis in this post is mine.)
Yes, they did indeed mischaracterize the probability, as I have been arguing for months.
I’ll send a link to this post, and Prof. Kaye’s article, to reporter Felch and to Jamie Gold. They have dismissed my complaints on this issue in the past. But it seems to me that they have to pay attention, now that an expert they have cited has stated in an academic article that the article “mischaracterized” the central statistic in the article.
P.S. You shouldn’t misread Prof. Kaye’s phrasing to indicate that he believes the match was indeed “almost worthless to the prosecution.” Further details, set forth in the extended entry, will dispel that notion. Those details are of interest mainly to those intensely interested in this topic. But I know there are a few of you among the regular readers here.
Prof. Kaye goes on to argue that the 1 in 3 statistic should not be of particular interest to jurors:
If logic were the life of the law, the np statistic would not be permitted. The figure of np = 1/3 in People v. Puckett, for instance, is an estimate of the probability that a database of profiles of n = 338,000 individuals would yield a hit to someone (not necessarily Puckett) if it were composed exclusively of individuals who are not the source of the crime-scene DNA (and who are not identical twins of the true source). [I will interpose here that I have repeatedly emphasized that the 1/3 statistic most properly and accurately represents a probability related to a database of innocent individuals. I have been ridiculed for that statement, but Prof. Kaye's statement supports me. -- Patterico] Unlike the random-match probability of p = 1/1,100,000, this number is not part of a likelihood ratio that is of interest to the jury. The legal issue, as the Supreme Court stated in Nelson, is not whether the database is innocent, but only whether the one defendant named Puckett is guilty or innocent. The likelihood ratio for the match with respect to Puckett as compared to a randomly selected individual is closer to 1,100,000 than to 3. (Kaye 2009). Thus, it is hard to see how the 1/3 figure is of much benefit to a juror seeking a reasonable explanation of the probative force of the evidence.
Prof. Kaye goes on to acknowledge that “the innocent-database-match probability is not completely irrelevant.” The whole thing makes interesting reading, and I recommend that you download it. My main point in this postscript is that Prof. Kaye clearly does not take the position that the 1/3 statistic is determinative and should be given great weight by jurors. To the contrary, he believes that statistic should be mostly (if not completely) irrelevant to jurors.