When statistical evidence is not specific enough

Abstract

Many philosophers have pointed out that statistical evidence, or at least some forms of it, lack desirable epistemic or non-epistemic properties, and that this should make us wary of litigations in which the case against the defendant rests in whole or in part on statistical evidence. Others have responded that such broad reservations about statistical evidence are overly restrictive since appellate courts have expressed nuanced views about statistical evidence. In an effort to clarify and reconcile, I put forward an interpretive analysis of why statistical evidence should raise concerns in some cases but not others. I argue that when there is a mismatch between the specificity of the evidence and the expected specificity of the accusation, statistical evidence-as any other kind of evidence-should be considered insufficient to sustain a conviction. I rely on different stylized court cases to illustrate the explanatory power of this analysis.

Introduction to recent issues in philosophy of statistics: evidence, testing, and applications

“…noulli's golden theorem. The second group of articles reflects on the uses of statistics in various fields, including Dethier (2022) on climate science, Di Bello (2021) on law, Gardiner and Zaharatos (2022) on epistemology, Park (2022) on medicine, and Watson (2022) on machine learning. In what follows, we offer brief summaries of the contributions to this topical collection.…”

“…In his contribution, reflecting on when statistical evidence is meant to establish a defendant's guilt, Marcello Di Bello (2021) proposes a theory of when to reject such evidence. He argues that if "there is a mismatch between the specificity of the evidence and the expected specificity of the accusation…" then evidence "should be considered insufficient to sustain a conviction unless it is adequately supplemented by other, more specific evidence" (12,252).…”