Bayes Theorem, Revisited

It won’t surprise some of you that I have an interest in evidence theory. I wrote, recently, on Slaw, about the use of Bayes Theorem in civil litigation in Canada. You will find that posting, as well as other discussions of Bayes Theorem on Slaw at this link.

I just learned of  – I stumbled across it looking for something else – a recent doctoral theses on the use of Bayes Theorem in litigation. (The Americans fought this battle more than a decade ago: see the paper here.) We’re slow in Canada. It must be the weather. It can’t be that Canada is a “slow zone” as in, for example, Vernor Vinge’s “A Fire Upon The Deep“.

In any event, it’s a Canadian thesis, to boot.

The thesis is: Helena Miriam Likwornik, “Bayes Rules – A Bayesian-Intuit Approach to Legal Evidence” (2011) <; or just click on this link. I’ve just begun to read it. (I know I’m supposed to be on vacation; however, vacationing properly is hard work and I’m on vacation from that, too.)

I’m going to quote the first two paragraphs of s. 1.1  because there is something Ms. Likwornik says in the second paragraph that sets up the comments with which I end this post.

The problem as I see it is this: statistical evidence is ever-more pervasive in our world and the law too often avoids or misuses it. The solution I offer to this problem involves both a diagnosis and proposed treatment plan. The diagnosis is the absence of a normative framework for integrating statistical evidence into legal reasoning. That is, no one model has achieved clear acceptance in establishing the standard for the correct manipulation of statistical evidence in the context of the law. The solution I suggest is as follows: (i) accept Bayesianism as a normative framework against which errors in considering statistical evidence may be evaluated; and (ii) encourage education within the legal community and amongst expert witnesses about common conceptual errors involving statistical evidence as well as techniques to limit their occurrence.

What follows is a presentation of support for this proposed solution. After primers on both the nature of legal evidence and Bayesianism, I tackle a series of “paradoxes” that are often used to dispute the relevance of Bayesianism to the law. I proceed to demonstrate that these paradoxes are far from inevitable and that Bayesianism and legal intuition can be aligned. This demonstration is essential to the proposal that Bayesianism can serve a useful role in the legal approach to statistical evidence; it is only if judges and juries can see and grasp the correctness of the Bayesian approach that it offers a plausible solution to the problem. A comparison is drawn to the medical community in which Bayesianism is accepted at a normative level while failures in application at the level of practice are acknowledged. The response to this in the medical community has been to increase education in numerical literacy for physicians. The law can learn something from this approach both in principle and at the level of practical tools that aid in understanding basic probability. Ultimately, I look at some specific applications to the law and conclude with recommendations both in relation to education and the presentation of evidence that can aid in putting statistical evidence to good legal use.

That may be so about physicians and the medical community at large. However, what Ms. Likwornik has  seems to have forgotten, as I read her paper, is the number of people who claim they went to law school because they couldn’t handle math. And consistently prove that.

[Deletion made and underlined words added in penultimate sentence on July 10, 2013: DC]


  1. David Cheifetz

    You’re welcome. I hope you have more success than I’ve had. Just be careful of windmills.

  2. Helena Likwornik

    “However, what Ms. Likwornik has forgotten is the number of people who claim they went to law school because they couldn’t handle math. And consistently prove that.” Unfortunately, I have not, nor can I, forget that. I just refuse to allow an explanation become an excuse. Thanks for reading!

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