Search Results

The presentation of expert forensic science evidence in rigorous statistical terms raises the question of how lay fact-finders (judges and jurors) might employ such evidence to prove events in issue. Can this simply be left to the common sense of fact-finders or should the law provide further guidance about how they should reason in applying the criminal standard of proof? Should courts demand that witnesses who give statistical evidence express that evidence in a particular form? This article examines the non-mathematical nature of common law fact-finding and its embodiment in the presumption of innocence principle underlying the criminal standard of proof. It argues that forensic scientists present evidence in a form that makes transparent the risks of error so that, in determining satisfaction of the accused’s guilt having regard to all the evidence before it, the fact-finder considers the reasonable possibility of doubts necessarily left open by statistical evidence.

Statistical methods aim to answer a variety of questions about observations. A simple example occurs when a fairly reliable test for a condition or substance, C, has given a positive result. Three important types of questions are: Should this observation lead me to believe that C is present? Does this observation justify my acting as if C were present? Is this observation evidence that C is present? This chapter distinguishes among these three questions in terms of the variables and principles that determine their answers. It then uses this framework to understand the scope and limitations of current methods for interpreting statistical data as evidence. By “statistical evidence,” we mean observations that are interpreted under a probability model. Questions of the third type, concerning the evidential interpretation of statistical data, are central to many applications of statistics in science. The chapter shows that for answering them, current statistical methods are seriously flawed. It looks for the source of the problems and proposes a solution based on the law of likelihood.

This chapter applies probabilistic knowledge to problems in legal and moral philosophy. It is argued that legal standards of proof require factfinders to know probabilistic contents. For instance, proof by a preponderance of the evidence requires knowledge that the defendant is at least .5 likely to be liable, whereas proof of guilt beyond a reasonable doubt requires knowledge of a significantly stronger content. The fact that legal proof requires knowledge explains why merely statistical evidence is insufficient to license a legal verdict of liability or guilt. In addition to explaining the limited value of statistical evidence, probabilistic knowledge is useful in spelling out norms violated by acts of racial and other profiling. It can be epistemically wrong to infer from statistics that a woman is probably an administrative assistant, for instance, even when inferring facts about ordinary objects from similar statistics is perfectly okay.

This chapter consists of three main parts. It first discusses the effect of various cognitive limitations, heuristics, and biases on the actual and perceived credibility of various types of evidence—including eyewitness testimonies, probabilistic data, and circumstantial evidence. It further examines the extent to which the use of expert testimonies can overcome such heuristics and biases. The second part analyzes behavioral aspects of burden-of-proof rules, such as the justification for placing the burden on the plaintiff, and the actual meaning of the standard of proof in civil and criminal proceedings. Finally, the third part argues that while people’s bounded rationality creates obstacles for judicial truth-finding, it also makes it much harder for interested parties, litigants and witnesses, to hide the truth—thus facilitating accurate fact-finding.

In this chapter, Michael Blome-Tillmann argues that embracing a knowledge-first approach can help to resolve important epistemological problems in legal philosophy. Blome-Tillmann takes, as a starting point, a puzzle arising from the evidential standard Preponderance of the Evidence and its application in civil procedure. The evidential standard captured by Preponderance of the Evidence is usually glossed as ‘greater than 0.5 given the admissible evidence’. But this characterization generates puzzles, where our intuitions about whether a defendant should be found liable diverge in case pairs where the evidential probability captured this way is the same. Blome-Tillmann argues that the tension generated by such puzzles can be resolved fairly straightforwardly within a knowledge-first framework.

This chapter draws on Hohfeld’s scheme of jural opposites and correlatives to identify the irreducibly second-personal nature of legal entitlements and the interplay between the right-holder’s authority and the duty-bearer’s accountability. Based on that insight, the author argues that facts that courts need to ascertain and—critically—the procedures that courts must carry out in ascertaining those facts, ought to be second-personal as well. As a corollary, courts must only rely upon second-personal evidence, that is: upon information concerning the alleged jural relationship between the holder of the underlying entitlement and the bearer of the correlative duty or obligation. Further, this fundamental requirement defines the modus operandi of the Anglo-American system of evidence and its core rules.

This chapter offers a review of scholarship concerning the proof paradoxes along with some critical comments of my own. Particular attention is given to problems of naked statistical evidence and the conjunction paradox, along with some thoughts on the discursive dilemma as applied to jury voting. For the sake of clarity, I try to separate what is known concerning human intuitions concerning these puzzles, how they are treated by existing law (to the extent they are noticed at all), and which results might be best from a normative perspective.

In this chapter, the risk minimisation conception of justification is criticised for its predictions about the force of statistical evidence, and it is argued that justification cannot be understood solely in terms of probability. A non-probabilistic support relation between evidence and propositions—termed normic support—is introduced and put forward as a necessary condition for justification. The distinction between normic support and probabilistic support turns out to be akin to the more widely recognised distinction between ceteris paribus laws and brute statistical generalisations. A formal model of normic support, involving a normalcy ordering of possible worlds, is proposed.

There has been much debate over the value of naked statistical evidence (NSE). However, while most scholars agree that it is problematic to base a verdict on NSE, they disagree on why it is problematic, pointing to different characteristics of NSE as the root of the problem. In the last decade, the debate has been energized by publications that propose a number of new approaches. This chapter joins the most recent debates on NSE—in particular, the objections to naked statistics based upon sensitivity, normalcy, incentives for lawful conduct, and free will. The authors argue that the problem of NSE is not an epistemic one, as some assume, but is actually a moral problem.

The belief that medicine derived from human parts could be used to increase someone's social power is fundamental to an understanding of the conspiracies investigated here. The first part of this chapter explores the roots of this belief in so far as they can be recovered from historical record. The second part seeks to interpret the statistical evidence on the incidence of medicine murder, as it is derived from various sources, and asks to what extent the observation made in the late 1940s of ‘a very startling increase’ in the phenomenon in that decade was justified.

This chapter concerns pernicious predictive inferences: taking someone to be likely to possess a socially disvalued trait based on statistical information about the prevalence of that trait within a social group to which she belongs. Some scholars have argued that pernicious predictive inferences are morally prohibited, but are sometimes epistemically required, leaving us with a tragic conflict between the requirements of epistemic rationality and those of morality. Others have responded by arguing that pernicious predictive inferences are sometimes epistemically prohibited. The present chapter takes a different approach, considering the sort of reluctance to draw pernicious predictive inferences that seems morally praiseworthy and vindicating its epistemic status. We argue that, even on a simple, orthodox Bayesian picture of the requirements of epistemic rationality, agents must consider the costs of error—including the associated moral and political costs—when forming and revising their credences. Our attitudes toward the costs of error determine how “risky” different credences are for us, and our epistemic states are justified in part by our attitudes toward epistemic risk. Thus, reluctance to draw pernicious predictive inferences need not be epistemically irrational, and the apparent conflict between morality and epistemic rationality is typically illusory.

Attempts to replicate reported studies often fail because the research relied on data mining—searching through data for patterns without any pre-specified, coherent theories. The perils of data mining can be exacerbated by data torturing—slicing, dicing, and otherwise mangling data to create patterns. If there is no underlying reason for a pattern, it is likely to disappear when someone attempts to replicate the study. Big data and powerful computers are part of the problem, not the solution, in that they can easily identify an essentially unlimited number of phantom patterns and relationships, which vanish when confronted with fresh data. If a researcher will benefit from a claim, it is likely to be biased. If a claim sounds implausible, it is probably misleading. If the statistical evidence sounds too good to be true, it probably is.

This chapter examines the implications of the reference-class problem for attempts to model the probative value of evidence in mathematical terms. This examination makes three distinct contributions to evidence scholarship. First, and most importantly, it articulates and explains the problematic relationship between algorithmic tools and legal decision-making. Second, it points out serious pitfalls to be avoided for analytical or empirical studies of juridical proof. Third, it indicates when algorithmic tools may be more or less useful in the evidentiary process. As such, the chapter offers yet another demonstration of the very complex set of relationships involving human knowledge and rationality, on the one hand, and attempts to reduce either to a set of formal concepts, on the other.

In this chapter Wilson presents statistical evidence of decline in such indices of popular religious interest as church membership, church attendance, and the religious sacralizing of such vital events as childbirth, marriage, and death. Although his definition of secularization rests on the social influence of religion, this focus suggests that institutional power and individual religious interest are intimately linked.

This chapter explores the origins and effects of the Pure Food and Drugs Act of 1906 in the U.S. It explains that the Act provided regulators unprecedented authority over interstate trade in food and drug products. This chapter investigates the timing and nature of support for the Act and evaluates the early enforcement of this law and its effects on consumer welfare. Both the narrative and statistical evidence suggest that a nuanced combination of the three main views of Progressive Era reform explains the adoption of the law.

In our evaluative framework, boredom with life as whole, as opposed to episodic of boredom we all experience on some occasions, is becoming a serious problem. Freedom and affluence have opened up possibilities of life for many more people than it was available ever before. Many of us waste it by watching mindless entertainment, shopping for unwanted bargains, recreational drug-taking, pornography, over-indulgence in alcohol, and following charlatans who sell salvation. Statistical evidence indicates that apathy oscillating with restlessness, loss of meaning, disaffection, disengagement, and estrangement are now widespread in our evaluative framework. The remedy is engagement in worthwhile activities, but the educational system that leaves students ignorant of the great achievements of art, history, and literature and the failure to distinguish between popular entertainment and goodness, beauty, and truth doom countless people in our evaluative framework to a dispiriting, boring lives.