Forensic Probability and Statistics - M1 - 8EC

Prerequisites

1. Basic course in (discrete and continuous) probability theory, including at least the following topics: events, random variables, conditional probability, independence. For instance, a course on the level of Sheldon Ross:" A first course in probability" suffices.

2. It may help if the student is familiar with the very basics of statistics, in particular basic estimation theory and hypotheses testing, but this is not strictly necessary.

3. No knowledge in biology, philosophy or forensic science is assumed since we will introduce the necessary background during the lectures.

Aim of the course

The aim of this course is to understand the basics of the theory and philosophy of forensic statistics and probability, and to apply these to concrete situations. The course is a natural mixture of rigorous probabilistic and statistical theory on the one hand, and elements from biology, forensic science, legal science, and philosophy on the other.

We plan to discuss the following topics:

 

  1. The philosophy of probability in a forensic context. The way we interpret probability is crucial for the applicability. We will argue that only an epistemic and subjective interpretation is tenable, and discuss consequences.
  2. The Bayesian framework with prior odds, posterior odds, and likelihood ratios. Likelihood ratios as measure of the strength of evidence.
  3. Understanding likelihood ratios and likelihood ratio distributions when likelihood ratios are interpreted as random variables.
  4. The probability and statistics of DNA evidence for trace-person matches, family relatedness, and mixed profiles.
  5. The role of p-values in reporting forensic evidence. In particular we address the issue that p-values do not measure the strength of evidence and should not be used for that purpose.
  6. The classical island problems: assigning evidential weight to a shared characteristic in a closed population. We distinguish between the cold case, when an individual is randomly selected, and the search case in which the first matching individual is considered. The difference is relevant for the evidential value of a match. Also, subpopulations are discussed.
  7. Population dynamics.
  8. Statistical and probabilistic aspects of database searches. Databases can lead to a direct match or may point at a relative of the donor of a DNA profile.
  9. The uncertainty of likelihood ratios and how to deal with this.
  10. Applications to forensic casework.

 

Rules about homework/Exam

There will be a written exam which counts for 70% of your final grade.

In addition, there are three mini-tests which count for 10% each and which are made in class.

In order to pass the course, you need to score at least a 5.0 for the exam. Any grade lower than 5.0 cannot be compensated by the mini-tests.

In case you need to do the resit, the rules remain the same. The mini-tests still count for 30%, and you need to score at least 5.0 for the resit to pass.

Lecture notes/Literature

We use the book Probability and Forensic Evidence - Theory, Philosophy, and Applications, by Ronald Meester and Klaas Slooten (Cambridge University Press 2021, 454 pages). We will post a complete pdf on the website. Additional material will be distributed during the course.

 

Lecturers

Ronald Meester (VU) and Klaas Slooten (NFI/VU)