Statistical Thinking for Forensic Practitioners - Session 1 (Fall 2022)

Overview of the course: Statistical Thinking for Forensic Practitioners.
Led by Hal Stern, Provost and Executive Vice Chancellor at UC Irvine and co-director at CSAFE.
Focus will be on the importance of probability and statistics in forensic science over the coming weeks.

Developments in forensic science necessitate a deeper understanding of statistics:
- National Academy of Sciences Report strengthened the call for scientific rigor in forensic science.
- PCAST report emphasized the need for further studies in the field.
Formation of OSAC (Organization of Scientific Area Committees) to standardize forensic practices, in collaboration with NIST.
CSAFE aims to develop statistical tools for forensic evidence evaluation.

Understanding the Daubert standard, which governs admission of scientific testimony.
Federal Rule of Evidence 702 emphasizes reliability and the necessity of sufficient facts or data in scientific evidence.
A solid grounding in probability helps address issues in forensic science.

The primary question addressed through the course:
- Source evaluation: Determining if two evidence samples originate from the same source.
Varied examples will be explored, including DNA, trace evidence, and pattern evidence.

Part 1: Probability in forensic science (unchanged except for a few slides).
Part 2: Data collection, reliability, and validity.
Part 3: Statistical concepts relevant to the two-stage approach to evidence analysis.
Part 4: Current discussions surrounding the likelihood ratio.
Emphasis on audience engagement and addressing questions throughout the course.

Probability defined: A number between 0 and 1 representing the likelihood of an event occurring.
Examples:
- Probability of specific weather conditions, winning sports events, and forensic evidence matching.
Two major interpretations of probability:
- Long-run frequency: Based on empirical observations.
- Subjective probability: Based on personal judgment or belief.
Key distinctions in interpreting probability:
- Independence of events can significantly affect probability calculations.

Robbery involving eyewitness testimony (visual and descriptive details).
Use of probability to argue the rarity of matching characteristics (1 in 12 million chance of survival).
Importance of clarifying assumptions of independence in calculating combined probabilities.

Difference between a population (all items of interest) and a sample (subset of the population analyzed).
Significance of understanding variation in repeat measurements and inter-finger comparisons in forensic statistics.

Defined as the probability of an event occurring, given that another event has occurred.
Example from personal experience regarding flight delays given specific weather conditions.
Practical implications for forensic science, especially when assessing probabilities tied to evidence outcomes.

Analysis of the death penalty assignment based on victim race:
- Conditional probability calculated within a two-by-two matrix context.
- Insight into how the victim's race correlated with the likelihood of receiving the death penalty.

Analysis of gunshot residue case to illustrate false positives and negatives.
Key takeaway: Reliability of tests and underlying probabilities significantly affect conclusions drawn in forensic contexts.

Definition: A method for updating probabilities based on new evidence.
Application in paternity tests and their implications in legal cases.
Ratio of likelihoods emphasizes the need for careful interpretation of evidence versus assumptions made.

Emphasis on understanding the basis of probabilities in forensic settings is critical.
Critical evaluation of the independence assumption in combined probabilistic assessments.
Distinct recognition of different probability types—evidence likelihood versus hypothesis probabilities.
Ongoing discussions and analyses in the courtroom based on these principles.

Reminder of future sessions focusing on data collection, reliability of forensic statistics, and the significance of likelihood ratios.
Invitations for continued engagement and inquiries throughout the course.