It is one thing to hold that merely statistical evidence is _sometimes_ insufficient for rational belief, as in typical lottery and profiling cases. It is another thing to hold that merely statistical evidence is _always_ insufficient for rational belief. Indeed, there are cases where statistical evidence plainly does justify belief. This project develops a dispositional account of the normativity of statistical evidence, where the dispositions that ground justifying statistical (...) class='Hi'>evidence are connected to the goals (= proper function) of objects. There are strong intuitive motivations for doing this. For we can turn almost any case of _non-justifying_ merely statistical evidence into a case of _justifying_ merely statistical evidence by adding information about the dispositions and goals of the objects involved. The resulting view not only helps us understand when and why merely statistical evidence is normatively significant, but it also helps us understand how statistical evidence relates to more standard forms of evidence (perceptual, testimonial). The emerging view also has surprising applications, as it imposes limitations on the epistemic value of fine-tuning arguments for theism as well as undermines a standard class of case-based arguments for moral encroachment. (shrink)

The relation of normic support offers a novel solution to the proof paradox: a paradox in evidence law arising from legal cases involving merely statistical evidence (Smith 2018). Central to the normic support solution has been the thesis that merely statistical evidence cannot confer normic support. However, it has been observed that there are exceptions to this: there exist cases where merely statistical evidence can give rise to normic support (Blome-Tillmann (...) 2020). If correct, this fact seems to undermine the normic support solution to the proof paradox. This paper explores a resolution: normic support can resolve the proof paradox even though merely statistical evidence sometimes gives rise to normic support. The key to understanding this resolution lies with a source of evidential support that arises out of bodies of evidence that involve *character evidence* (Colyvan, et al. 2001). It turns out that character evidence can provide normic support when it is grounded in our knowledge of a certain kind of disposition individuals can have: *goal-directed dispositions*. The upshot is the recovery of the normic support solution to the puzzle of whether statistical evidence can meet legal standards of proof. (shrink)

In order to perform certain actions – such as incarcerating a person or revoking parental rights – the state must establish certain facts to a particular standard of proof. These standards – such as preponderance of evidence and beyond reasonable doubt – are often interpreted as likelihoods or epistemic confidences. Many theorists construe them numerically; beyond reasonable doubt, for example, is often construed as 90 to 95% confidence in the guilt of the defendant. -/- A family of influential cases (...) suggests standards of proof should not be interpreted numerically. These ‘proof paradoxes’ illustrate that purely statistical evidence can warrant high credence in a disputed fact without satisfying the relevant legal standard. In this essay I evaluate three influential attempts to explain why merely statistical evidence cannot satisfy legal standards. (shrink)

Over almost a half-century, evidence law scholars and philosophers have contended with what have come to be called the “Proof Paradoxes.” In brief, the following sort of paradox arises: Factfinders in criminal and civil trials are charged with reaching a verdict if the evidence presented meets a particular standard of proof—beyond a reasonable doubt, in criminal cases, and preponderance of the evidence, in civil trials. It seems that purely statistical evidence can suffice for just such (...) a level of certainty in a variety of cases where our intuition is that it would nonetheless be wrong to convict the defendant, or find in favor of the plaintiff, on merely statistical evidence. So, we either have to convict with statistical evidence, in spite of an intuition that this is unsettling, or else explain what (dispositive) deficiency statistical evidence has. -/- Most scholars have tried to justify the resistance to relying on merely statistical evidence: by relying on epistemic deficiencies in this kind of evidence; by relying on court practice; and also by reference to the psychological literature. In fact, I argue, the epistemic deficiencies philosophers and legal scholars allege are suspect. And, I argue, while scholars often discuss unfairness to civil defendants, they ignore a long history of relying on statistical evidence in a variety of civil matters, including employment discrimination, toxic torts, and market share liability cases. Were the dominant arguments in the literature to prevail, it would extremely difficult for plaintiffs to recover in a variety of cases. -/- The various considerations I advance lead to the conclusion that when it comes to naked statistical evidence, philosophers and legal scholars who argue for its insufficiency have been caught with their pants down. (shrink)

There is something puzzling about statistical evidence. One place this manifests is in the law, where courts are reluctant to base affirmative verdicts on evidence that is purely statistical, in spite of the fact that it is perfectly capable of meeting the standards of proof enshrined in legal doctrine. After surveying some proposed explanations for this, I shall outline a new approach – one that makes use of a notion of normalcy that is distinct from the (...) idea of statistical frequency. The puzzle is not, however, merely a legal one. Our unwillingness to base beliefs on statistical evidence is by no means limited to the courtroom, and is at odds with almost every general principle that epistemologists have proposed as to how we ought to manage our beliefs. (shrink)

The replication crisis has prompted many to call for statistical reform within the psychological sciences. Here we examine issues within Frequentist statistics that may have led to the replication crisis, and we examine the alternative—Bayesian statistics—that many have suggested as a replacement. The Frequentist approach and the Bayesian approach offer radically different perspectives on evidence and inference with the Frequentist approach prioritising error control and the Bayesian approach offering a formal method for quantifying the relative strength of (...) class='Hi'>evidence for hypotheses. We suggest that rather than mere statistical reform, what is needed is a better understanding of the different modes of statistical inference and a better understanding of how statistical inference relates to scientific inference. (shrink)

Cancer research is experiencing ‘paradigm instability’, since there are two rival theories of carcinogenesis which confront themselves, namely the somatic mutation theory and the tissue organization field theory. Despite this theoretical uncertainty, a huge quantity of data is available thanks to the improvement of genome sequencing techniques. Some authors think that the development of new statistical tools will be able to overcome the lack of a shared theoretical perspective on cancer by amalgamating as many data as possible. We think (...) instead that a deeper understanding of cancer can be achieved by means of more theoretical work, rather than by merely accumulating more data. To support our thesis, we introduce the analytic view of theory development, which rests on the concept of plausibility, and make clear in what sense plausibility and probability are distinct concepts. Then, the concept of plausibility is used to point out the ineliminable role played by the epistemic subject in the development of statistical tools and in the process of theory assessment. We then move to address a central issue in cancer research, namely the relevance of computational tools developed by bioinformaticists to detect driver mutations in the debate between the two main rival theories of carcinogenesis. Finally, we briefly extend our considerations on the role that plausibility plays in evidence amalgamation from cancer research to the more general issue of the divergences between frequentists and Bayesians in the philosophy of medicine and statistics. We argue that taking into account plausibility-based considerations can lead to clarify some epistemological shortcomings that afflict both these perspectives. (shrink)

This entry introduces the Lockean Thesis and sketches the ways in which the lottery paradox, the preface paradox, and the problem of merely statistical evidence can be used to put pressure on the Lockean Thesis.

Michael Huemer argues, on statistical grounds, that ``existence is evidence for immortality". On reasoning derived from the anthropic principle, however, mere existence cannot be evidence against any non-indexical, ``eternal'' hypothesis that predicts observers. This note attempts to advertise the much-flouted anthropic principle's virtues and workings in a new way, namely by calling attention to the fact that it is the primary intension of one's indexically-described evidence that best characterizes one's epistemic position.

The law views with suspicion statistical evidence, even evidence that is probabilistically on a par with direct, individual evidence that the law is in no way suspicious of. But it has proved remarkably hard to either justify this suspicion, or to debunk it. In this paper, we connect the discussion of statistical evidence to broader epistemological discussions of similar phenomena. We highlight Sensitivity – the requirement that a belief be counterfactually sensitive to the truth (...) in a specific way – as a way of epistemically explaining the legal suspicion towards statistical evidence. Still, we do not think of this as a satisfactory vindication of the reluctance to rely on statistical evidence. Knowledge – and Sensitivity, and indeed epistemology in general – are of little, if any, legal value. Instead, we tell an incentive-based story vindicating the suspicion towards statistical evidence. We conclude by showing that the epistemological story and the incentive-based story are closely and interestingly related, and by offering initial thoughts about the role of statistical evidence in morality. (shrink)

Recently, the practice of deciding legal cases on purely statistical evidence has been widely criticised. Many feel uncomfortable with finding someone guilty on the basis of bare probabilities, even though the chance of error might be stupendously small. This is an important issue: with the rise of DNA profiling, courts are increasingly faced with purely statistical evidence. A prominent line of argument—endorsed by Blome-Tillmann 2017; Smith 2018; and Littlejohn 2018—rejects the use of such evidence by (...) appealing to epistemic norms that apply to individual inquirers. My aim in this paper is to rehabilitate purely statistical evidence by arguing that, given the broader aims of legal systems, there are scenarios in which relying on such evidence is appropriate. Along the way I explain why popular arguments appealing to individual epistemic norms to reject legal reliance on bare statistics are unconvincing, by showing that courts and individuals face different epistemic predicaments (in short, individuals can hedge when confronted with statistical evidence, whilst legal tribunals cannot). I also correct some misconceptions about legal practice that have found their way into the recent literature. (shrink)

Martin Smith has recently proposed, in this journal, a novel and intriguing approach to puzzles and paradoxes in evidence law arising from the evidential standard of the Preponderance of the Evidence. According to Smith, the relation of normic support provides us with an elegant solution to those puzzles. In this paper I develop a counterexample to Smith’s approach and argue that normic support can neither account for our reluctance to base affirmative verdicts on bare statistical evidence (...) nor resolve the pertinent paradoxes. Normic support is, as a consequence, not a successful epistemic anti-luck condition. (shrink)

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. (shrink)

According to the Rational Threshold View, a rational agent believes p if and only if her credence in p is equal to or greater than a certain threshold. One of the most serious challenges for this view is the problem of statistical evidence: statistical evidence is often not sufficient to make an outright belief rational, no matter how probable the target proposition is given such evidence. This indicates that rational belief is not as sensitive to (...) statistical evidence as rational credence. The aim of this paper is twofold. First, we argue that, in addition to playing a decisive role in rationalizing outright belief, non-statistical evidence also plays a preponderant role in rationalizing credence. More precisely, when both types of evidence are present in a context, non-statistical evidence should receive a heavier weight than statistical evidence in determining rational credence. Second, based on this result, we argue that a modified version of the Rational Threshold View can avoid the problem of statistical evidence. We conclude by suggesting a possible explanation of the varying sensitivity to different types of evidence for belief and credence based on the respective aims of these attitudes. (shrink)

Recent attempts to resolve the Paradox of the Gatecrasher rest on a now familiar distinction between individual and bare statistical evidence. This paper investigates two such approaches, the causal approach to individual evidence and a recently influential (and award-winning) modal account that explicates individual evidence in terms of Nozick's notion of sensitivity. This paper offers counterexamples to both approaches, explicates a problem concerning necessary truths for the sensitivity account, and argues that either view is implausibly committed (...) to the impossibility of no-fault wrongful convictions. The paper finally concludes that the distinction between individual and bare statistical evidence cannot be maintained in terms of causation or sensitivity. We have to look elsewhere for a solution of the Paradox of the Gatecrasher. (shrink)

Abstract. Suppose that the word of an eyewitness makes it 80% probable that A committed a crime, and that B is drawn from a population in which the incidence rate of that crime is 80%. Many philosophers and legal theorists have held that if this is our only evidence against those parties then (i) we may be justified in finding against A but not against B; but (ii) that doing so incurs a loss in the accuracy of our findings. (...) This paper argues against (ii). It argues that accuracy considerations can motivate taking different attitudes towards individualized and statistical evidence even across cases where they generate the same probability that the defendant is guilty. (shrink)

The paper takes a closer look at the role of knowledge and evidence in legal theory. In particular, the paper examines a puzzle arising from the evidential standard Preponderance of the Evidence and its application in civil procedure. Legal scholars have argued since at least the 1940s that the rule of the Preponderance of the Evidence gives rise to a puzzle concerning the role of statistical evidence in judicial proceedings, sometimes referred to as the Problem (...) of Bare Statistical Evidence. While this puzzle has led to the development of a multitude of accounts and approaches in the legal literature, I argue here that the problem can be resolved fairly straightforwardly within a knowledge-first framework. (shrink)

Many who think that naked statistical evidence alone is inadequate for a trial verdict think that use of probability is the problem, and something other than probability – knowledge, full belief, causal relations – is the solution. I argue that the issue of whether naked statistical evidence is weak can be formulated within the probabilistic idiom, as the question whether likelihoods or only posterior probabilities should be taken into account in our judgment of a case. This (...) question also identifies a major difference between the Process Reliabilist and Probabilistic Tracking views of knowledge and other concepts. Though both are externalist, and probabilistic, epistemic theories, Tracking does and Process Reliabilism does not put conditions on likelihoods. So Tracking implies that a naked statistic is not adequate evidence about an individual, and does not yield knowledge, whereas the Reliabilist thinks it gives justified belief and knowledge. Not only does the Tracking view imply that naked statistical evidence is insufficient for a verdict, but it gives us resources to explain why, in terms of risk and the special conditions of a trial. (shrink)

Many philosophers have argued that statistical evidence regarding group characteristics (particularly stereotypical ones) can create normative conflicts between the requirements of epistemic rationality and our moral obligations to each other. In a recent article, Johnson-King and Babic argue that such conflicts can usually be avoided: what ordinary morality requires, they argue, epistemic rationality permits. In this article, we show that as data get large, Johnson-King and Babic’s approach becomes less plausible. More constructively, we build on their project and (...) develop a generalized model of reasoning about stereotypes under which one can indeed avoid normative conflicts, even in a big data world, when data contain some noise. In doing so, we also articulate a general approach to rational belief updating for noisy data. (shrink)

In some cases, there appears to be an asymmetry in the evidential value of statistical and more individualized evidence. For example, while I may accept that Alex is guilty based on eyewitness testimony that is 80% likely to be accurate, it does not seem permissible to do so based on the fact that 80% of a group that Alex is a member of are guilty. In this paper I suggest that rather than reflecting a deep defect in (...) class='Hi'>statistical evidence, this asymmetry might arise from a general constraint on rational inquiry. Plausibly the degree of evidential support needed to justify taking a proposition to be true depends on the stakes of error. While relying on statistical evidence plausibly raises the stakes by introducing new kinds of risk to members of the reference class, paradigmatically `individualized' evidence---evidence tracing back to A's voluntary behavior---can lower the stakes. The net result explains the apparent evidential asymmetry without positing a deep difference in the brute justificatory power of different types of evidence. (shrink)

Statistical evidence—say, that 95% of your co-workers badmouth each other—can never render resenting your colleague appropriate, in the way that other evidence (say, the testimony of a reliable friend) can. The problem of statistical resentment is to explain why. We put the problem of statistical resentment in several wider contexts: The context of the problem of statistical evidence in legal theory; the epistemological context—with problems like the lottery paradox for knowledge, epistemic impurism and (...) doxastic wrongdoing; and the context of a wider set of examples of responses and attitudes that seem not to be appropriately groundable in statistical evidence. Regrettably, we do not come up with a fully general, fully adequate, fully unified account of all the phenomena discussed. But we give reasons to believe that no such account is forthcoming, and we sketch a somewhat messier account that may be the best that can be had here. (shrink)

Discusses whether legal proof is merely probabilistic, focusing on the famous proof paradox.

A question, long discussed by legal scholars, has recently provoked a considerable amount of philosophical attention: ‘Is it ever appropriate to base a legal verdict on statistical evidence alone?’ Many philosophers who have considered this question reject legal reliance on bare statistics, even when the odds of error are extremely low. This paper develops a puzzle for the dominant theories concerning why we should eschew bare statistics. Namely, there seem to be compelling scenarios in which there are multiple (...) sources of incriminating statistical evidence. As we conjoin together different types of statistical evidence, it becomes increasingly incredible to suppose that a positive verdict would be impermissible. I suggest that none of the dominant views in the literature can easily accommodate such cases, and close by offering a diagnosis of my own. (shrink)

Why can testimony alone be enough for findings of liability? Why statistical evidence alone can’t? These questions underpin the “Proof Paradox” (Redmayne 2008, Enoch et al. 2012). Many epistemologists have attempted to explain this paradox from a purely epistemic perspective. I call it the “Epistemic Project”. In this paper, I take a step back from this recent trend. Stemming from considerations about the nature and role of standards of proof, I define three requirements that any successful account in (...) line with the Epistemic Project should meet. I then consider three recent epistemic accounts on which the standard is met when the evidence rules out modal risk (Pritchard 2018), normic risk (Ebert et al. 2020), or relevant alternatives (Gardiner 2019 2020). I argue that none of these accounts meets all the requirements. Finally, I offer reasons to be pessimistic about the prospects of having a successful epistemic explanation of the paradox. I suggest the discussion on the proof paradox would benefit from undergoing a ‘value-turn’. (shrink)

I explore how rational belief and rational credence relate to evidence. I begin by looking at three cases where rational belief and credence seem to respond differently to evidence: cases of naked statistical evidence, lotteries, and hedged assertions. I consider an explanation for these cases, namely, that one ought not form beliefs on the basis of statistical evidence alone, and raise worries for this view. Then, I suggest another view that explains how belief and (...) credence relate to evidence. My view focuses on the possibilities that the evidence makes salient. I argue that this makes better sense of the difference between rational credence and rational belief than other accounts. (shrink)

This essay is an accessible introduction to the proof paradox in legal epistemology. -/- In 1902 the Supreme Judicial Court of Maine filed an influential legal verdict. The judge claimed that in order to find a defendant culpable, the plaintiff “must adduce evidence other than a majority of chances”. The judge thereby claimed that bare statistical evidence does not suffice for legal proof. -/- In this essay I first motivate the claim that bare statistical evidence (...) does not suffice for legal proof. I then introduce and motivate a knowledge-centred explanation of this fact. The knowledge-centred explanation rests on two premises. The first is that legal proof requires knowledge of culpability. The second is that one cannot attain knowledge that p from bare statistical evidence that p. To motivate the second premise, I suggest that beliefs based on bare statistical evidence fail to be safe—they could easily be wrong—and bare statistical evidence cannot eliminate relevant alternatives. -/- I then cast doubt on the first premise; I argue that legal proof does not require knowledge. I thereby dispute the knowledge-centred explanation of the inadequacy of bare statistical evidence for legal proof. Instead of appealing to the nature of knowledge, I suggest we should seek a more direct explanation by appealing to those more foundational epistemic properties, such as safety or eliminating relevant alternatives. (shrink)

Statistical evidence is crucial throughout disparate impact’s three-stage analysis: during (1) the plaintiff’s prima facie demonstration of a policy’s disparate impact; (2) the defendant’s job-related business necessity defense of the discriminatory policy; and (3) the plaintiff’s demonstration of an alternative policy without the same discriminatory impact. The circuit courts are split on a vital question about the “practical significance” of statistics at Stage 1: Are “small” impacts legally insignificant? For example, is an employment policy that causes a one (...) percent disparate impact an appropriate policy for redress through disparate impact litigation? This circuit split calls for a comprehensive analysis of practical significance testing across disparate impact’s stages. Importantly, courts and commentators use “practical significance” ambiguously between two aspects of practical significance: the magnitude of an effect and confidence in statistical evidence. For example, at Stage 1 courts might ask whether statistical evidence supports a disparate impact (a confidence inquiry) and whether such an impact is large enough to be legally relevant (a magnitude inquiry). Disparate impact’s texts, purposes, and controlling interpretations are consistent with confidence inquires at all three stages, but not magnitude inquiries. Specifically, magnitude inquiries are inappropriate at Stages 1 and 3—there is no discriminatory impact or reduction too small or subtle for the purposes of the disparate impact analysis. Magnitude inquiries are appropriate at Stage 2, when an employer defends a discriminatory policy on the basis of its job-related business necessity. (shrink)

There has long been an impression that reliabilism implies externalism and that frequentist statistics is externalist because it is reliabilist. I argue, however, that frequentist statistics can be plausibly understood as a form of internalist reliabilism -- internalist in the conventional sense but reliabilist in certain unconventional yet intriguing ways. More importantly, I develop the thesis that reliabilism does not imply externalism, not by stretching the meaning of 'reliabilism' merely to break the implication, but in order to better understand (...) frequentist statistics, which represents one of the most sustained attempts by scientists to develop an epistemology for their own use. (shrink)

There are two competing views regarding the role of mechanistic knowledge in inferences about the effectiveness of interventions. One view holds that inferences about the effectiveness of interventions should be based only on data from population-level studies (often statistical evidence from randomised trials). The other view holds that such inferences must be based in part on mechanistic evidence. The competing views are local principles of inference, the plausibility of which can be assessed by a more general normative (...) principle of inference. Bayesianism tells us to base inferences on both the ‘likelihood’ and the ‘prior’. The likelihood represents statistical evidence. One influence on the prior probability of a hypothesis like ‘d causes x’ is mechanistic knowledge of how d causes x. Thus, reasoning about such inferences by appealing to both statistical and mechanistic evidence is vindicated by our best general theory of inference. The primary contribution of this paper is to assess the merits and weaknesses of the arguments on both sides of the debate, using the Bayesian framework. This analysis lends support to those who argue that we should base our causal inferences about interventions in part on mechanistic evidence. (shrink)

A popular informal argument suggests that statistics about the preponderance of criminal involvement among particular demographic groups partially justify others in making defensive mistakes against members of the group. One could worry that evidence-relative accounts of moral rights vindicate this argument. After constructing the strongest form of this objection, I offer several replies: most demographic statistics face an unmet challenge from reference class problems, even those that meet it fail to ground non-negligible conditional probabilities, even if they did, they (...) introduce new costs likely to cancel out any justificatory contribution of the statistic, but even if they didn’t, demographic facts are the wrong sort to make a moral difference to agents’ negative rights. I conclude that the popular argument should be rejected, and evidence-relative theories do not have the worrisome implication. (shrink)

Many oppose the use of profile evidence against defendants at trial, even when the statistical correlations are reliable and the jury is free from prejudice. The literature has struggled to justify this opposition. We argue that admitting profile evidence is objectionable because it violates what we call “equal protection”—that is, a right of innocent defendants not to be exposed to higher ex ante risks of mistaken conviction compared to other innocent defendants facing similar charges. We also show (...) why admitting other forms of evidence, such as eyewitness, trace, and motive evidence, does not violate equal protection. (shrink)

This chapter will review selected aspects of the terrain of discussions about probabilities in statistical mechanics (with no pretensions to exhaustiveness, though the major issues will be touched upon), and will argue for a number of claims. None of the claims to be defended is entirely original, but all deserve emphasis. The first, and least controversial, is that probabilistic notions are needed to make sense of statistical mechanics. The reason for this is the same reason that convinced Maxwell, (...) Gibbs, and Boltzmann that probabilities would be needed, namely, that the second law of thermodynamics, which in its original formulation says that certain processes are impossible, must, on the kinetic theory, be replaced by a weaker formulation according to which what the original version deems impossible is merely improbable. Second is that we ought not take the standard measures invoked in equilibrium statistical mechanics as giving, in any sense, the correct probabilities about microstates of the system. We can settle for a much weaker claim: that the probabilities for outcomes of experiments yielded by the standard distributions are effectively the same as those yielded by any distribution that we should take as a representing probabilities over microstates. Lastly, (and most controversially): in asking about the status of probabilities in statistical mechanics, the familiar dichotomy between epistemic probabilities (credences, or degrees of belief) and ontic (physical) probabilities is insufficient; the concept of probability that is best suited to the needs of statistical mechanics is one that combines epistemic and physical considerations. (shrink)

Our evidential environments are reflections of our social contexts. This is important because the evidence we encounter influences the beliefs we form. But, traditional epistemologists have paid little attention to the generation of this evidential environment, assuming that it is irrelevant to epistemic normativity. This assumption, I argue, is dangerous. Idealizing away the evidential environment obscures the ways that our social contexts distort its contents. Such social distortion can lead to evidential oppression, an epistemic injustice arising from the ubiquity (...) of ideologically-inflected portrayals of oppressed social groups. In some cases, this distortion can be so pervasive as to create a skeptical pothole in agents' epistemic environments---a limited region in which a skeptical scenario obtains. The skeptical challenge of social distortion is important because it suggests that prejudice and irrationality alone may not be enough to explain many harmful beliefs. Where justified belief is merely a matter of responding well to one's evidence, the ubiquity of ideologically-inflected evidence may impel a matching doxastic state, even for exemplary epistemic agents. Nevertheless, there is an asymmetry in blameworthiness between the Cartesian victim and someone laboring under evidential oppression. This asymmetry reveals the need for distinctively non-ideal epistemic norms. I offer a characterization of one such norm: the internalist practice of self-stewardship. (shrink)

The major competing statistical paradigms share a common remarkable but unremarked thread: in many of their inferential applications, different probability interpretations are combined. How this plays out in different theories of inference depends on the type of question asked. We distinguish four question types: confirmation, evidence, decision, and prediction. We show that Bayesian confirmation theory mixes what are intuitively “subjective” and “objective” interpretations of probability, whereas the likelihood-based account of evidence melds three conceptions of what constitutes an (...) “objective” probability. (shrink)

In the face of continuing assumptions by many scientists and journal editors that p-values provide a gold standard for inference, counter warnings are published periodically. But the core problem is not with p-values, per se. A finding that “p-value is less than α” could merely signal that a critical value has been exceeded. The question is why, when estimating a parameter, we provide a range (a confidence interval), but when testing a hypothesis about a parameter (e.g. µ = x) (...) we proceed as if “=” entails exact equality of the parameter with x. That standard is hard to meet, and is not a standard expected for power calculations, where we are satisfied to reject a null hypothesis H0 if the result is merely “detectably” different from (exact) H0. This paper explores, with resampling (simulation) methods, the impacts on p-values, and alternatives, if the null hypothesis is defined as a thick or thin range of values. It also examines, empirically, the extent to which the p-value may or may not be a good predictor of the probability that H0 is true, given the distribution of the data. (shrink)

Gignac and Zajenkowski (2020) find that “the degree to which people mispredicted their objectively measured intelligence was equal across the whole spectrum of objectively measured intelligence”. This Comment shows that Gignac and Zajenkowski’s (2020) finding of homoscedasticity is likely the result of a recoding choice by the experimenters and does not in fact indicate that the Dunning-Kruger Effect is a mere statistical artifact. Specifically, Gignac and Zajenkowski (2020) recoded test subjects’ responses to a question regarding self-assessed comparative IQ onto (...) a linear IQ scale when a normal IQ scale would likely have been more appropriate. More generally, researchers studying self-assessed intelligence should be aware of potential measurement problems that may arise when transforming an ordinal scale onto an interval scale. (shrink)

Extant literature on Goodman’s ‘New Riddle of Induction’ deals mainly with two versions. I consider both of them, starting from the (‘epistemic’) version of Goodman’s classic of 1954. It turns out that it belongs to the realm of applications of inductive logic, and that it can be resolved by admitting only significant evidence (as I call it) for confirmations of hypotheses. Sect. 1 prepares some ground for the argument. As much of it depends on the notion of evidential significance, (...) this concept is defined and its introduction motivated. Further, I introduce and explain the distinction between support and confirmation: put in a slogan, ‘confirmation is support by significant evidence’. The second section deals with the Riddle itself. I demonstrate that, given the provisions of the first section, not ‘anything confirms anything’: significant green-evidence confirms only green-hypotheses (and no grue-hypotheses), and significant grue-evidence confirms only grue-hypotheses (and no green-hypotheses), whichever terms we use for expressing these evidences or hypotheses. The third section rounds off my treatment. First I show that Frank Jackson’s use of his counterfactual condition is unsuccessful. Further, I argue that no unwanted consequences result, if one starts from the other, ‘objective’, definition of ‘grue’, as it constitutes no more than a mere fact of logic that cannot do any harm. Finally, I present a grue-case involving both kinds of definition, where the exclusive confirmation of either the green- or the grue-hypothesis is shown. (shrink)

The evidence-based medicine (EBM) movement is touted as a new paradigm in medical education and practice, a description that carries with it an enthusiasm for science that has not been seen since logical positivism flourished (circa 1920–1950). At the same time, the term ‘‘evidence-based medicine’’ has a ring of obviousness to it, as few physicians, one suspects, would claim that they do not attempt to base their clinical decision-making on available evidence. However, the apparent obviousness of EBM (...) can and should be challenged on the grounds of how ‘evidence’ has been problematised in the philosophy of science. EBM enthusiasm, it follows, ought to be tempered. The post-positivist, feminist, and phenomenological philosophies of science that are examined in this paper contest the seemingly unproblematic nature of evidence that underlies EBM by emphasizing different features of the social nature of science. The appeal to the authority of evidence that characterizes evidence-based practices does not increase objectivity but rather obscures the subjective elements that inescapably enter all forms of human inquiry. The seeming common sense of EBM only occurs because of its assumed removal from the social context of medical practice. In the current age where the institutional power of medicine is suspect, a model that represents biomedicine as politically disinterested or merely scientific should give pause. (shrink)

Public policymakers and institutional decision-makers routinely face questions about whether interventions “work”: does universal basic income improve people’s welfare and stimulate entrepreneurial activity? Do gated alleyways reduce burglaries or merely shift the crime burden to neighbouring communities? What is the most cost-effective way to improve students’ reading abilities? These are empirical questions that seem best answered by looking at the world, rather than trusting speculations about what will be effective. Evidence-based policy (EBP) is a movement that concretizes this (...) intuition. It maintains that policy should be based on evidence of “what works.” Not any evidence will do, however. Following on the heels of its intellectual progenitor, evidence-based medicine (EBM), EBP insists on the use of high-quality evidence produced in accordance with rigorous methodological standards. Though intuitively compelling, EBP has attracted significant criticism from methodologists, political scientists, and philosophers. This chapter provides a critical overview of EBP through a philosophical lens, reviewing and discussing some of the most pressing challenges that EBP faces and outlining some proposals for improving it. I first provide a brief overview of EBP and distinguish a broader and narrower understanding of it. I then review existing lines of criticism of EBP, two of which are considered in detail. The first elaborates how EBP struggles with extrapolation, that is, the use of evidence from study populations to make inferences about the effects of policies on novel target populations. The second maintains that EBP’s methodological tenets are deeply entwined with moral and political values, which can threaten EBP’s promise to promote objectivity in policymaking. Finally, I make some proposals for how EBP could be improved going forward. (shrink)

Evidence based-policy (EBP) is a popular research paradigm in the applied social sciences and within government agencies. Informally, EBP represents an explicit commitment to applying scientific methods to public affairs, in contrast to ideologically-driven or merely intuitive “common-sense” approaches to public policy. More specifically, the EBP paradigm places great weight on the results of experimental research designs, especially randomised controlled trials (RCTs), and systematic literature reviews that place evidential weight on experimental results. One hope is that such research (...) designs and approaches to analysing the scientific literature are sufficiently robust that they can settle what really ‘works’ in public policy. Can EBP succeed in displacing reliance on domain-specific expertise? On our account, this is seldom, if ever, the case. The key reason for this is that underlying this approach is generally an appeal to argument by induction, which always requires further assumptions to underwrite its validity, and if not induction, some other argument form that also requires assumptions that are very often not validated for the case at hand. (shrink)

ABSTRACTIn this article, we discuss the benefits of Bayesian statistics and how to utilize them in studies of moral education. To demonstrate concrete examples of the applications of Bayesian statistics to studies of moral education, we reanalyzed two data sets previously collected: one small data set collected from a moral educational intervention experiment, and one big data set from a large-scale Defining Issues Test-2 survey. The results suggest that Bayesian analysis of data sets collected from moral educational studies can provide (...) additional useful statistical information, particularly that associated with the strength of evidence supporting alternative hypotheses, which has not been provided by the classical frequentist approach focusing on P-values. Finally, we introduce several practical guidelines pertaining to how to utilize Bayesian statistics, including the utilization of newly developed free statistical software, Jeffrey’s Amazing Statistics Program, and thresholding based on Bayes Factors, to scholars in the field of moral education. (shrink)

The full Bayesian signi/cance test (FBST) for precise hypotheses is presented, with some illustrative applications. In the FBST we compute the evidence against the precise hypothesis. We discuss some of the theoretical properties of the FBST, and provide an invariant formulation for coordinate transformations, provided a reference density has been established. This evidence is the probability of the highest relative surprise set, “tangential” to the sub-manifold (of the parameter space) that defines the null hypothesis.

Homeopathic medications are used by millions, and hundreds of millions of dollars are spent on these remedies in the USA alone. In the UK, the NHS covers homeopathic treatments. Nonetheless, homeopathy is held in considerable disrepute by much of the medical and scientific community.Many proponents of homeopathy are well aware of these criticisms but remain unimpressed. The differences of opinion run deep, and the debate seems deadlocked. We aim to shed some light on this situation. We briefly recap some of (...) the major arguments on each side, but we try to go further by making explicit an underlying philosophical presupposition. In particular, we will claim that there is an important principle, which has ancient roots going back at least to Occam, some version of which constrains all empirical reasoning.We call this constraint the simplicity principle.We argue that this is not something specific to a scientific paradigm, but that, all of us, including proponents of homeopathy, are themselves deeply committed to the simplicity principle. However, once the simplicity principle is made explicit and applied to homeopathy, allegiance to homeopathy is clearly seen as irrational. The point is not merely the lack of clinical trials supporting homeopathy; rather, belief in the efficacy of homeopathy leaves a mountain of unexplained mysteries, and thereby flies in the face of the simplicity rule that guides the homeopaths’ own reasoning and arguments. If nothing else, we hope that defenders of homeopathy will gain a greater understanding of why critics are so deeply reluctant to accept the efficacy of homeopathic interventions – and that this reluctance is not mere stubbornness or artificial allegiance to western medicine. Finally, we also hope thereby to illustrate the usefulness of philosophy in unearthing presuppositions in seemingly deadlocked debates. (shrink)

Many people have recently argued that we need to distinguish between experiences and seemings and that this has consequences for views about how perception provides evidence. In this article I spell out my take on these issues by doing three things. First, I distinguish between mere sensations like seeing pitch black all around you and perceptual experiences like seeing a red apple. Both have sensory phenomenology in presenting us with sensory qualities like colors, being analog in Dretske's sense, and (...) being fine-grained. However, only the latter have furthermore a perceptual phenomenology characterized by objectification and related dualities of perspectivality/completion and variation/constancy. Second, I elaborate on the reasons for thinking that both mere sensations and perceptual experiences need to be distinguished from accompanying seemings that passively assign things into conceptual categories and thereby tell you something about them. For example, when you look at a red apple and have the relevant recognitional abilities it will also normally seem to you that this is an apple. Finally, I argue that the best version of the popular dogmatist view about evidence is one which claims that it's neither experiences nor seemings by themselves, but rather the right sorts of composites of experiences and seemings that provide evidence. (shrink)

ABSTRACTIn a recent paper, Michael Pardo argues that the epistemic property that is legally relevant is the one called Safety, rather than Sensitivity. In the process, he argues against our Sensitivity-related account of statistical evidence. Here we revisit these issues, partly in order to respond to Pardo, and partly in order to make general claims about legal epistemology. We clarify our account, we show how it adequately deals with counterexamples and other worries, we raise suspicions about Safety's value (...) here, and we revisit our general skepticism about the role that epistemological considerations should play in determining legal policy. (shrink)

Agent-causal libertarians maintain we are irreducible agents who, by acting, settle matters that aren’t already settled. This implies that the neural matters underlying the exercise of our agency don’t conform to deterministic laws, but it does not appear to exclude the possibility that they conform to statistical laws. However, Pereboom (Noûs 29:21–45, 1995; Living without free will, Cambridge University Press, Cambridge, 2001; in: Nadelhoffer (ed) The future of punishment, Oxford University Press, New York, 2013) has argued that, if these (...) neural matters conform to either statistical or deterministic physical laws, the complete conformity of an irreducible agent’s settling of matters with what should be expected given the applicable laws would involve coincidences too wild to be credible. Here, I show that Pereboom’s argument depends on the assumption that, at times, the antecedent probability certain behavior will occur applies in each of a number of occasions, and is incapable of changing as a result of what one does from one occasion to the next. There is, however, no evidence this assumption is true. The upshot is the wild coincidence objection is an empirical objection lacking empirical support. Thus, it isn’t a compelling argument against agent-causal libertarianism. (shrink)

DOI: 10.1080/00031305.2018.1564697 When the editors of Basic and Applied Social Psychology effectively banned the use of null hypothesis significance testing (NHST) from articles published in their journal, it set off a fire-storm of discussions both supporting the decision and defending the utility of NHST in scientific research. At the heart of NHST is the p-value which is the probability of obtaining an effect equal to or more extreme than the one observed in the sample data, given the null hypothesis and (...) other model assumptions. Although this is conceptually different from the probability of the null hypothesis being true, given the sample, p-values nonetheless can provide evidential information, toward making an inference about a parameter. Applying a 10,000-case simulation described in this article, the authors found that p-values’ inferential signals to either reject or not reject a null hypothesis about the mean (α = 0.05) were consistent for almost 70% of the cases with the parameter’s true location for the sampled-from population. Success increases if a hybrid decision criterion, minimum effect size plus p-value (MESP), is used. Here, rejecting the null also requires the difference of the observed statistic from the exact null to be meaningfully large or practically significant, in the researcher’s judgment and experience. The simulation compares performances of several methods: from p-value and/or effect size-based, to confidence-interval based, under various conditions of true location of the mean, test power, and comparative sizes of the meaningful distance and population variability. For any inference procedure that outputs a binary indicator, like flagging whether a p-value is significant, the output of one single experiment is not sufficient evidence for a definitive conclusion. Yet, if a tool like MESP generates a relatively reliable signal and is used knowledgeably as part of a research process, it can provide useful information. (shrink)

For the past half-century, both jurisprudence and epistemology have been haunted by questions about why individual evidence (i.e., evidence which picks out a specific individual) can sufficiently justify a guilty or liable verdict while bare statistical evidence (i.e., statistical evidence which does not pick out a specific individual) does not sufficiently justify such a verdict. This thesis examines three popular justifications for such a disparity in verdicts – Judith Jarvis Thomson’s causal account, Enoch et (...) al.’s sensitivity account, and Sarah Moss’ knowledge-first account, before critiquing each in turn. After such an analysis, the thesis then defends the claim that legal verdicts require the factfinder (e.g., the judge or jury) to have a justified de re belief (i.e., a belief about a specific object – namely the defendant), and that this doxastic requirement justifies the disparity in rulings, as it is epistemically insufficient to justify a de re belief based on bare statistical evidence alone. A brief account of how these beliefs are formed and spread is also given. After making such a distinction, the thesis then formalizes the burdens of proof of the preponderance of the evidence and beyond a reasonable doubt using the de re/de dicto distinction. Finally, the thesis pre-empts possible objections, namely by providing an account of DNA evidence as individual evidence and giving an account of how false convictions can occur on the de re view of legal proof. (shrink)

This article analyses how the French Academy of Sciences assessed Jaime Ferrán’s cholera vaccine submitted for the Prix Bréant in the 1880s. Ferrán, a Spanish independent physician, discovered the treatment in 1884 and tried it on thousands of patients during the cholera outbreak in Valencia the following year. His evaluation sparked a controversy in Spain and abroad on the vaccine’s efficacy. The Bréant jury did not see any evidence for it in Ferrán’s submission, a decision usually interpreted in terms (...) of French scientific nationalism (or simple chauvinism): an outsider from the scientific periphery could not be awarded the Bréant. Drawing on the archival records of the award, we suggest that Ferrán failed instead to provide data that the Academy could consider unbiased, according to the contemporary standards for data presentation. We will illustrate these standards at work in the assessment of another submission from Spain, by Philipp Hauser, who received the Bréant for the thoroughness of his statistical endeavour. (shrink)

In recent years, the idea has been gaining ground that our traditional conceptions of knowledge and cognition are unduly limiting, in that they privilege what goes on inside the ‘skin and skull’ of an individual reasoner. Instead, it has been argued, knowledge and cognition need to be understood as embodied, situated, and extended. Whether these various interrelations and dependencies are ‘merely’ causal, or are in a more fundamental sense constitutive of knowledge and cognition, is as much a matter of (...) controversy as the degree to which they pose a challenge to ‘traditional’ conceptions of cognition, knowledge and the mind. In this paper we argue that when the idea of ‘extendedness’ is applied to a core concept in epistemology and the philosophy of science—namely, scientific evidence—things appear to be on a much surer footing. The evidential status of data gathered through extended processes—including its utility as justification or warrant—do not seem to be weakened by virtue of being extended, but instead are often strengthened because of it. Indeed, it is often precisely by virtue of this extendedness that scientific evidence grounds knowledge claims, which individuals may subsequently ascribe to themselves. The functional equivalence between machine-based gathering, filtering, and processing of data and human interpretation and assessment is the crucial factor in deciding whether evidence has been gathered, rather than the distinction between intra- and extracranial processes or individual and social processes. To prioritize biological processes here, and to assert the superiority of human cognitive capacities seems both arbitrary and unwarranted with respect to gathering evidence, and ultimately would lead to an unattractive skepticism about many of the methods used in science to gather evidence. In other words, conceiving of scientific evidence as ‘impersonal’ not only better captures the character of evidence-gathering in practice, but also makes sense of a large amount of evidence-gathering that ‘personal’ accounts fail to either acknowledge or accurately describe. Whilst we suggest it is likely that all internally-distributed evidence-gathering processes are merely contingently internal processes, a significant number of externally-distributed evidence-gathering processes are necessarily externally-distributed. Some evidence can only be gathered by extended epistemic agents. (shrink)