Tuesday, July 10, 2012

If the Shoe Fits, You Must Not Calculate It (Part II)

The Court of Appeal in R. v. T. did not like Mr. Ryder’s truncated and standardized testimony (outlined in Part I). It complained about the supposedly small size of the FSS database, the recourse to a formula for combining information on different features, the mixture of objective and subjective probabilities in arriving at the undisclosed conditional probability of 1/100 and the unstated likelihood ratio of 100, the lack of the references in his testimony and reports to these computations and the FSS likelihood table, and the use of the honorific "scientific” in front of “evidence” and “support." In summarizing its reasons for judging the conviction "unsafe," the court emphasized the issue of transparency and completeness, writing that "the practice of using a Bayesian approach and likelihood ratios to formulate opinions placed before a jury without that process being disclosed and debated in court is contrary to principles of open justice."

Although Mr. Ryder insisted that he had merely used the figures to confirm what his "very extensive experience of footwear marks" already told him, the court saw the effort to reason more explicitly about the match as ammunition for cross-examination. Concluding that this cross-examination could have changed the outcome of the trial, the Court of Appeal quashed the conviction and ordered a retrial.

It suggested that on retrial, testimony not billed as scientific and based strictly on personal experience about the fact that the defendant’s Nike trainers “could have” been the source of the impressions would be acceptable. Beyond this, the court seemed willing to countenance "a more definite evaluative opinion" — as long as the "size or pattern" is "unusual" based on "years studying this kind of comparison." That kind of opinion would be fine, the court wrote, because "[i]t is a judgment based on his experience"  and "without any figures or mathematical formula."

There is much to criticize in this court’s reasoning. As many authors have noted, surely an expert whose intuitive or experiential impressions give rise to a judgment about the source hypothesis should be encouraged to consult the available statistical data and to consider their limitations to produce a fully informed judgment.

Less prominent in the writing on the case is the fact that, despite a description to the court, from Visiting Professor and Scientist and Scholar Allan Jamieson, of Mr. Ryder's "approach as 'the Bayesian approach' of using likelihood ratios," likelihoodism and Bayesianism are hardly the same. As explained in the previous posting (Part I), Mr. Ryder never spoke of prior or posterior odds or of source probabilities. He merely attached some words—"modest support"—to his unstated estimate of the likelihood ratio.The court's condemnation of "the practice of using a Bayesian approach" therefore seems inapposite.

To be sure, "Bayesianism" can be used to motivate the likelihood ratio as a measure of probative value, but that does not make the simple presentation of a likelihood ratio “the Bayesian approach.” In fact, the likelihood school of statistical inference abjures the use of prior probability distributions, and it does not use Bayes' rule in coming to decisions about hypotheses. Likelihoodism maintains that the statistician should be concerned only with whether the evidence provides increased or decreased support for one hypothesis over another. A likelihoodist would find the presentation of the likelihood ratio itself, without any Bayesian baggage or interpretation, entirely appropriate.

Of course, this is not to say that either the likelihoodist or the Bayesian would agree that the particular likelihood ratio kept out of sight in R. v. T. should be admissible. Although the likelihoodist would be pleased that Mr. Ryder's LR of 100 and his description of it as "moderate ... support" were untainted by a subjective, prior probability, the objectivity or accuracy of the estimate and the adjective could be a source of legitimate concern.

But the concern is not with the use of likelihoods per se. Casting doubt on a particular estimate of an LR does not make it appropriate for the expert to speak of the source probability—quantitatively or qualitatively. Indeed, if the expert lacks the data and experience with which to estimate the likelihood ratio, as the court in R. v. T. suggested, how can the expert have anything useful to say about the source probability? The court's preference for expert opinions on source probabilities simply sweeps the problem under the proverbial rug.

References on R. v. T.

C.E.H. Berger et al., Evidence Evaluation: A Response to the Court of Appeal Judgment in R v T, 51 Sci. & Justice 43 (2011)

F. Hoar et al., Extending the Confusion about Bayes, 74 Modern L. Rev. 444 (2011)

David H. Kaye, Likelihoodism, Bayesianism, and a Pair of Shoes, 53 Jurimetrics J. (forthcoming Fall 2012)

G.S. Morrison, The Likelihood-ratio Framework and Forensic Evidence in Court: A Response to R v T, 16 Int'l J. Evid. & Proof 1 (2012)

Mike Redmayne et al., Forensic Science Evidence in Questions, 2011 Crim. L.R. 347

References on Likelihoodism

Jeffrey D. Blume, Likelihood Methods for Measuring Statistical Evidence, 21 Stat. Med. 2563 (2002)

Anthony W.F. Edwards, Likelihood (2d ed. 1992)

James Hawthorne, Inductive Logic, in Stanford Encyclopedia of Philosophy (Edward N. Zalta ed. 2012)

Richard M. Royall, Statistical Evidence: a Likelihood Paradigm (1997)

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