Adam Chilton, the Howard G. Krane Professor of Law, recently released a book, Trial by Numbers: A Lawyer’s Guide to Statistical Evidencewhich he made together with Kyle Rozema, law professor at Northwestern University. The book aims to provide lawyers and other legal professionals with a basic understanding of the most common methods of interpreting empirical evidence used in academic papers, policy briefs and expert witness reports.

Chilton recently answered a few questions about his new book, published by Oxford University Press in May 2024.

What inspired you to write? Try it by the numbers?

The ability to understand and interact with statistical evidence is increasingly important for almost every type of practice area in the legal profession. Data, statistics, and economic evidence play an important role in legal fields ranging from complex civil litigation to criminal defense to transactional law. But many lawyers do not gain a strong understanding of these topics as students, and law schools often do little to teach their students about them. Kyle Rozema and I thus wanted to write a book to make proof and empirical methods accessible to all members of the legal profession.

How does it Try it by the numbers help lawyers without strong math background engage with statistical evidence?

Most books on empirical methods say they will be simple and easy to understand, and then within a page or two there are complicated equations and statistics. We wanted to write a book that was about the intuition behind statistical concepts with as few equations or complicated math as possible. There is actually only one equation in the entire book – although it appears a few times. We also exclusively use examples that are drawn from legal education, the legal system and legal practice – which we hope will make the material more attractive and easier to understand for the average lawyer.

What role do you see statistical evidence playing in the future of legal proceedings?

Trials are already frequently “battles of the experts” in which both sides present expert witnesses who report their own analysis of the data or describe existing bodies of research. I see no sign that this reality will reverse in the near future. Instead, the increasing availability of data and academic research suggests that litigation will increasingly focus on arguments between experts who base their opinions on empirical evidence.

Can you provide an example from the book where statistical analysis played a critical role in a real case?

To teach the basics of regression analysis, we draw on the description (a slightly simplified version) of the facts in the recent case of Students for Fair Admissions v. Harvard. This was the case that ultimately led the US Supreme Court to rule that the use of race-based affirmative action in university admissions violates the Equal Protection Clause. Although the Supreme Court’s opinions did not go into the data’s mind, at the trial level, the case was a battle between two leading economists who used regression analysis to assess whether Harvard discriminated against students during the admissions process. We used these expert reports as the basis for how we explain the regression.

What are the most common mistakes lawyers make when dealing with statistical evidence, and how does the book help prevent them?

I’m not sure if it’s the most common mistake (I wish the data would make that kind of claim), but a common mistake made by lawyers, judges, and juries is to focus on whether a claimed statistical relationship is “statistically significant” in place to worry if the relationship is significantly significant. In other words, some relationships may be statistically significant but trivial in magnitude, but other relationships may be below the traditional level of statistical significance but substantial in size. We should shift our focus on litigation to worry more about effect size.

What advice do you have for law students or early career lawyers who want to improve their understanding of statistical evidence?

Well, the most obvious is to buy and read our book. But another entry point is to find an area in which you already have interests—such as politics, sports, investing, or even pop culture—and start exploring empirical works written on those topics that are designed for a popular audience. I can’t tell you how much data I know that has started to be applied to things like advanced basketball stats or election predictions. This is one way to make learning more about statistics feel like fun instead of homework.

What are some challenges lawyers face when presenting statistical evidence in court, and how can they overcome them?

Tables of numbers often make people’s eyes glaze over. If possible, it is extremely helpful to turn statistical results into figures and graphs instead of tables and lists of numbers. This makes the results more engaging and easier to understand. The goal should be to make the trial exhibits more similar New York Times infographics rather than the regression tables often found in medical research articles.

Anything else you’d like to share about the book?

We have tried to make this the most accessible book by introducing available empirical methods. So I hope it is helpful not only to members of the legal profession potentially interested in the subject, but to anyone else as well.