The 11th International Conference on Forensic Inference and Statistics, or ICFIS 2023, is set for June 12-15 of this year. It will be held at the Faculty of Law (Juridicum) of Lund University, Lund, Sweden. While I am saddened that I cannot attend this particular meeting, several years ago I had the pleasure of going to the 2014 International Conference on Forensic Inference and Statistics, or ICFIS which was the 9th iteration of the conference. I wrote a blog post about that meeting some time ago.
I can say, based on past experience alone, that this meeting is well worth attending. That’s particularly true if you are interested in the logical approach to evidence evaluation, but it would benefit any forensic scientist. You will not find a better collection of brilliant people all focused on forensic inference, in the broadest sense.
Forensic scientists, lawyers, academics—they will all be there.
David H. Kaye (DHK) is one of my favourite writers. He is truly prolific and always manages to provide great insights for the reader. His grasp of statistics, logic, and the law is second-to-none, and his ability to communicate those very challenging topics to his audience is equally impressive.
As a mini introduction, David “…is Distinguished Professor, and Weiss Family Scholar in the School of Law, a graduate faculty member of Penn State’s Forensic Science Program, and a Regents’ Professor Emeritus, ASU.” If you would like to see a list of his publications check out http://personal.psu.edu/dhk3/cv/cv_pubs.html
Yes, DHK has written many things on many topics. But I would like to focus on his less formal writings from his blog Forensic Science, Statistics & the Law.
In 1958 Ordway Hilton participated in Session #5 of the RCMP Seminar Series. His article was originally published in that series by the RCMP, and subsequently republished in 1995 in the International Journal of Forensic Document Examiners.
The later republication included the following abstract:
In every handwriting identification we are dealing with the theory of probability. If an opinion is reached that two writings are by the same person, we are saying in effect that with the identification factors considered the likelihood of two different writers having this combination of writing characteristics in common is so remote that for all practical purposes it can be disregarded. Such an opinion is derived from our experience and is made without formal reference to any mathematical measure. However, the mathematician provides us with a means by which the likelihood of chance duplication can be measured. It is the purpose of this paper to explore the possibility of applying such mathematical measure to the handwriting identification problem to see how we might quantitatively measure the likelihood of chance duplication.
Hilton’s article was written in 8 main sections with references, and is followed by a discussion between seminar participants. Today’s review will discuss each section of the article in turn.
The R program for statistics is an amazingly powerful and completely free program (under the terms of the Free Software Foundation’s GNU General Public License). If you have any need to do statistics, then you really must take a look at R or, more formally, “The R Project for Statistical Computing“.
What exactly is R? Simply put, “R is a language and environment for statistical computing and graphics.” It is a special open-source implementation of S which is one of the earlier statistical programming languages.
It has oft been said that “there are three kinds of lies: lies, damned lies and statistics”. That phrase, according to Mark Twain, came from Benjamin Disraeli. Interestingly, it has never been found in Disraeli’s written works so that attribution is likely incorrect.
A lie, perhaps, by Twain?
But I digress. The source of the statement doesn’t really matter. It is enough that the phrase reflects the belief that many people have when they think about statistics. It is a catchy little phrase. Yet most reasonable people know that numbers — and statistics are simply numbers after all — cannot do anything on their own. Hence, statistics can no more lie than they can sing or dance.
Forensic scientists, individually and as a group, unquestionably want to be completely logical, open and transparent in their approach to the evaluation of evidence. Further, I am sure that most document examiners believe this is exactly what they are achieving when they apply the procedures outlined in various traditional textbooks or the SWGDOC/ ASTM standards; for example, the SWGDOC Standard for Examination of Handwritten Items. Given the very understandable desire to be logical, I find it strange that so many people have a negative attitude towards anything “Bayesian” in nature. After all, a logical approach to evidence evaluation that conforms to the overall Bayesian philosophy or approach is, quite literally, the embodiment of logic (more specifically, probabilistic logic).