From Group Data to Useful Probabilities: The Relevance of Actuarial Risk Assessment in Individual Instances

Abstract

Probability plays a ubiquitous role in decision-making through a process in which we use data from groups of past outcomes to make inferences about new situations. Yet in recent years, many forensic mental health professionals have become persuaded that overly wide confidence intervals render actuarial risk assessment instruments virtually useless in individual assessments. If this were true, the mathematical properties of probabilistic judgments would preclude forensic clinicians from applying group-based findings about risk to individuals. As a consequence, actuarially based risk estimates might be barred from use in legal proceedings. Using a fictional scenario, I seek to show how group data have an obvious application to individual decisions. I also explain how misunderstanding the aims of risk assessment has led to mistakes about how, when, and why group data apply to individual instances. Although actuarially based statements about individuals' risk have many pitfalls, confidence intervals pose no barrier to using actuarial tools derived from group data to improve decision-making about individual instances.