How should we reason in science? Jan Sprenger and Stephan Hartmann offer a refreshing take on classical topics in philosophy of science, using a single key concept to explain and to elucidate manifold aspects of scientific reasoning. They present good arguments and good inferences as being characterized by their effect on our rational degrees of belief. Refuting the view that there is no place for subjective attitudes in 'objective science', Sprenger and Hartmann explain the value of convincing evidence in terms (...) of a cycle of variations on the theme of representing rational degrees of belief by means of subjective probabilities (and changing them by Bayesian conditionalization). In doing so, they integrate Bayesian inference—the leading theory of rationality in social science—with the practice of 21st century science. Bayesian Philosophy of Science thereby shows how modeling such attitudes improves our understanding of causes, explanations, confirming evidence, and scientific models in general. It combines a scientifically minded and mathematically sophisticated approach with conceptual analysis and attention to methodological problems of modern science, especially in statistical inference, and is therefore a valuable resource for philosophers and scientific practitioners. (shrink)

This text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis.

The lack of public support for climate change policies and refusals to vaccinate children are just two alarming illustrations of the impacts of dissent about scientific claims. Dissent can lead to confusion, false beliefs, and widespread public doubt about highly justified scientific evidence. Even more dangerously, it has begun to corrode the very authority of scientific consensus and knowledge. Deployed aggressively and to political ends, some dissent can intimidate scientists, stymie research, and lead both the public and policymakers to oppose (...) important public policies firmly rooted in science. -/- To criticize dissent is, however, a fraught exercise. Skepticism and fearless debate are key to the scientific process, making it both vital and incredibly difficult to characterize and identify dissent that is problematic in its approach and consequences. Indeed, as de Melo-Martín and Intemann show, the criteria commonly proposed as means of identifying inappropriate dissent are flawed and the strategies generally recommended to tackle such dissent are not only ineffective but could even make the situation worse. -/- The Fight Against Doubt proposes that progress on this front can best be achieved by enhancing the trustworthiness of the scientific community and by being more realistic about the limits of science when it comes to policymaking. It shows that a richer understanding of the context in which science operates is needed to disarm problematic dissent and those who deploy it. This, the authors argue, is the best way forward, rather than diagnosing the many instances of wrong-headed dissent. (shrink)

Medical nihilism is the view that we should have little confidence in the effectiveness of medical interventions. Jacob Stegenga argues persuasively that this is how we should see modern medicine, and suggests that medical research must be modified, clinical practice should be less aggressive, and regulatory standards should be enhanced.

Data from medical research are typically summarized with various types of outcome measures. We present three arguments in favor of absolute over relative outcome measures. The first argument is from cognitive bias: relative measures promote the reference class fallacy and the overestimation of treatment effectiveness. The second argument is decision-theoretic: absolute measures are superior to relative measures for making a decision between interventions. The third argument is causal: interpreted as measures of causal strength, absolute measures satisfy a set of desirable (...) properties, but relative measures do not. Absolute outcome measures outperform relative measures on all counts. (shrink)

In this influential study of central issues in the philosophy of science, Paul Horwich elaborates on an important conception of probability, diagnosing the failure of previous attempts to resolve these issues as stemming from a too-rigid conception of belief. Adopting a Bayesian strategy, he argues for a probabilistic approach, yielding a more complete understanding of the characteristics of scientific reasoning and methodology. Presented in a fresh twenty-first-century series livery, and including a specially commissioned preface written by Colin Howson, illuminating its (...) enduring importance and relevance to philosophical enquiry, this engaging work has been revived for a new generation of readers. (shrink)

Exploring the role of values in scientific inquiry, Hugh Lacey examines the nature and meaning of values, and looks at challenges to the view, posed by postmodernists, feminists, radical ecologists, Third-World advocates and religious fundamentalists, that science is value free. He also focuses on discussions of 'development', especially in Third World countries. This paperback edition includes a new preface.

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections (...) and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence and systems from The Cognitive Science Society. (shrink)

When interests and preferences of researchers or their sponsors cause bias in experimental design, data interpretation or dissemination of research results, we normally think of it as an epistemic shortcoming. But as a result of the debate on science and values, the idea that all extra-scientific influences on research could be singled out and separated from pure science is now widely believed to be an illusion. I argue that nonetheless, there are cases in which research is rightfully regarded as epistemologically (...) deficient due to the influence of preferences on its outcomes. I present examples from biomedical research and offer an analysis in terms of social epistemology. (shrink)

Measuring the effectiveness of medical interventions faces three epistemological challenges: the choice of good measuring instruments, the use of appropriate analytic measures, and the use of a reliable method of extrapolating measures from an experimental context to a more general context. In practice each of these challenges contributes to overestimating the effectiveness of medical interventions. These challenges suggest the need for corrective normative principles. The instruments employed in clinical research should measure patient-relevant and disease-specific parameters, and should not be sensitive (...) to parameters that are only indirectly relevant. Effectiveness always should be measured and reported in absolute terms (using measures such as 'absolute risk reduction'), and only sometimes should effectiveness also be measured and reported in relative terms (using measures such as 'relative risk reduction')-employment of relative measures promotes an informal fallacy akin to the base-rate fallacy, which can be exploited to exaggerate claims of effectiveness. Finally, extrapolating from research settings to clinical settings should more rigorously take into account possible ways in which the intervention in question can fail to be effective in a target population. (shrink)

For decades, cigarette companies helped to promote the impression that there was no scientific consensus concerning the safety of their product. The appearance of controversy, however, was misleading, designed to confuse the public and to protect industry interests. Created scientific controversies emerge when expert communities are in broad agreement but the public perception is one of profound scientific uncertainty and doubt. In the first book-length analysis of the concept of a created scientific controversy, David Harker explores issues including climate change, (...) Creation science, the anti-vaccine movement and genetically modified crops. Drawing on work in cognitive psychology, social epistemology, critical thinking and philosophy of science, he shows readers how to better understand, evaluate, and respond to the appearance of scientific controversy. His book will be a valuable resource for students of philosophy of science, environmental and health sciences, and social and natural sciences. (shrink)

Selling science has become a common practice in contemporary universities. This commodification of academia pervades many aspects of higher education, including research, teaching, and administration. As such, it raises significant philosophical, political, and moral challenges. This volume offers the first book-length analysis of this disturbing trend from a philosophical perspective and presents views by scholars of philosophy of science, social and political philosophy, and research ethics. The epistemic and moral responsibilities of universities, whether for-profit or nonprofit, are examined from several (...) philosophical standpoints. The contributors discuss the pertinent epistemological and methodological questions, the sociopolitical issues of the organization of science, the tensions between commodified practices and the ideal of “science for the public good,” and the role of governmental regulation and personal ethical behavior. In order to counter coercive and corruptive influences of academic commodification, the contributors consider alternatives to commodified research and offer practical recommendations for establishing appropriate research standards, methodologies and institutional arrangements, and a corresponding normative ethos. (shrink)

"This volume emerged from workshops held at Pennsylvania State University in 2003 and Stanford University in 2005"--P. vii.

This is an important book precisely because there is none other quite like it.

The U.S. scientific community has long led the world in research on such areas as public health, environmental science, and issues affecting quality of life. These scientists have produced landmark studies on the dangers of DDT, tobacco smoke, acid rain, and global warming. But at the same time, a small yet potent subset of this community leads the world in vehement denial of these dangers. -/- Merchants of Doubt tells the story of how a loose-knit group of high-level scientists and (...) scientific advisers, with deep connections in politics and industry, ran effective campaigns to mislead the public and deny well-established scientific knowledge over four decades. Remarkably, the same individuals surface repeatedly-some of the same figures who have claimed that the science of global warming is "not settled" denied the truth of studies linking smoking to lung cancer, coal smoke to acid rain, and CFCs to the ozone hole. "Doubt is our product," wrote one tobacco executive. These "experts" supplied it. -/- Naomi Oreskes and Erik M. Conway, historians of science, roll back the rug on this dark corner of the American scientific community, showing how ideology and corporate interests, aided by a too-compliant media, have skewed public understanding of some of the most pressing issues of our era. (shrink)

There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes’s original paper to contemporary formal learning theory.In (...) a paper published posthumously in 1763, the Reverend Thomas Bayes made a seminal contribution to the understanding of "analogical or inductive reasoning." Building on his insights, modem Bayesians have developed an account of scientific inference that has attracted numerous champions as well as numerous detractors. Earman argues that Bayesianism provides the best hope for a comprehensive and unified account of scientific inference, yet the presently available versions of Bayesianisin fail to do justice to several aspects of the testing and confirming of scientific theories and hypotheses. By focusing on the need for a resolution to this impasse, Earman sharpens the issues on which a resolution turns. John Earman is Professor of History and Philosophy of Science at the University of Pittsburgh. (shrink)

Claims that science should be more democratic than it is frequently arouse opposition. In this essay, I distinguish my own views about the democratization of science from the more ambitious theses defended by Paul Feyerabend. I argue that it is unlikely that the complexity of some scientific debates will allow for resolution according to the methodological principles of any formal confirmation theory, suggesting instead that major revolutions rest on conflicts of values. Yet these conflicts should not be dismissed as irresoluble.

More than 40 primary studies, and three recent systematic reviews and meta-analyses, have shown a clear association between pharmaceutical industry funding of clinical trials and pro-industry results. Industry sponsorship biases published scientific research in favour of the sponsors, a result of the strong interest commercial sponsors have in obtaining favourable results.Three proposed remedies to this problem are widely agreed upon among those concerned with the level of sponsorship bias: financial disclosure, reporting standards and trial registries. This paper argues that all (...) of these remedies either fail to address the mechanisms by which pharmaceutical companies’ sponsorship leads to biased results—design bias, multiple trials with predictable outcomes, fraud, rhetorical effects and publication bias—or else only inadequately address those mechanisms. As a result, the policies normally proposed for dealing with sponsorship bias are unable to eliminate it. Only completely separating public clinical research from pharmaceutical industry funding can eliminate sponsorship bias. (shrink)

APA PsycNET abstract: This is the first volume of a two-volume work on Probability and Induction. Because the writer holds that probability logic is identical with inductive logic, this work is devoted to philosophical problems concerning the nature of probability and inductive reasoning. The author rejects a statistical frequency basis for probability in favor of a logical relation between two statements or propositions. Probability "is the degree of confirmation of a hypothesis (or conclusion) on the basis of some given evidence (...) (or premises)." Furthermore, all principles and theorems of inductive logic are analytic, and the entire system is to be constructed by means of symbolic logic and semantic methods. This means that the author confines himself to the formalistic procedures of word and symbol systems. The resulting sentence or language structures are presumed to separate off logic from all subjectivist or psychological elements. Despite the abstractionism, the claim is made that if an inductive probability system of logic can be constructed it will have its practical application in mathematical statistics, and in various sciences. 16-page bibliography. (PsycINFO Database Record (c) 2016 APA, all rights reserved). (shrink)

Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.

Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.

“Probabilistic Causation” designates a group of theories that aim to characterize the relationship between cause and effect using the tools of probability theory. The central idea behind these theories is that causes change the probabilities of their effects. This article traces developments in probabilistic causation, including recent developments in causal modeling. A variety of issues within, and objections to, probabilistic theories of causation will also be discussed.

Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive inference as opposed to inductive behavior. We argue that the relevance of error probabilities (...) is to ensure that only statistical hypotheses that have passed severe or probative tests are inferred from the data. The severity criterion supplies a meta-statistical principle for evaluating proposed statistical inferences, avoiding classic fallacies from tests that are overly sensitive, as well as those not sensitive enough to particular errors and discrepancies. Introduction and overview 1.1 Behavioristic and inferential rationales for Neyman–Pearson (N–P) tests 1.2 Severity rationale: induction as severe testing 1.3 Severity as a meta-statistical concept: three required restrictions on the N–P paradigm Error statistical tests from the severity perspective 2.1 N–P test T(): type I, II error probabilities and power 2.2 Specifying test T() using p-values Neyman's post-data use of power 3.1 Neyman: does failure to reject H warrant confirming H? Severe testing as a basic concept for an adequate post-data inference 4.1 The severity interpretation of acceptance (SIA) for test T() 4.2 The fallacy of acceptance (i.e., an insignificant difference): Ms Rosy 4.3 Severity and power Fallacy of rejection: statistical vs. substantive significance 5.1 Taking a rejection of H0 as evidence for a substantive claim or theory 5.2 A statistically significant difference from H0 may fail to indicate a substantively important magnitude 5.3 Principle for the severity interpretation of a rejection (SIR) 5.4 Comparing significant results with different sample sizes in T(): large n problem 5.5 General testing rules for T(), using the severe testing concept The severe testing concept and confidence intervals 6.1 Dualities between one and two-sided intervals and tests 6.2 Avoiding shortcomings of confidence intervals Beyond the N–P paradigm: pure significance, and misspecification tests Concluding comments: have we shown severity to be a basic concept in a N–P philosophy of induction? (shrink)

Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the information sources. (...) Measurement of the coherence of information is a controversial matter: arguably, the more coherent a set of information is, the more confident we may be that its content is true, other things being equal. The authors offer a new treatment of coherence which respects this claim and shows its relevance to scientific theory choice. Bovens and Hartmann apply this methodology to a wide range of much discussed issues regarding evidence, testimony, scientific theories, and voting. Bayesian Epistemology is an essential tool for anyone working on probabilistic methods in philosophy, and has broad implications for many other disciplines. (shrink)

Evidentialism is a view about the conditions under which a person is epistemically justified in having a particular doxastic attitude toward a proposition. Evidentialism holds that the justified attitudes are determined entirely by the person's evidence. This is the traditional view of justification. It is now widely opposed. The essays included in this volume develop and defend the tradition.Evidentialism has many assets. In addition to providing an intuitively plausible account of epistemic justification, it helps to resolve the problem of the (...) criterion, helps to disentangle epistemic and ethical evaluations, and illuminates the relationship between epistemic evaluations of beliefs and the evaluation of the methods used to form beliefs. These issues are all addressed in the essays presented here. External world skepticism poses the classic problem for an epistemological theory. The final essay in this volume argues that evidentialism is uniquely well qualified to make sense of skepticism and to respond to its challenge.Evidentialism is a version of epistemic internalism. Recent epistemology has included many attacks on internalism and has seen the development of numerous externalist theories. The essays included here respond to those attacks and raise objections to externalist theories, especially the principal rival, reliabilism. Internalism generally has been criticized for having unacceptable deontological implications, for failing to connect epistemic justification to truth, and for failing to provide an adequate account of what makes basic beliefs justified. Each of these charges is answered in these essays. The collection includes two previously unpublished essays and new afterwords to five of the reprinted essays; it will be the definitive resource on evidentialism for all epistemologists. (shrink)

Although epistemic values have become widely accepted as part of scientific reasoning, non-epistemic values have been largely relegated to the "external" parts of science (the selection of hypotheses, restrictions on methodologies, and the use of scientific technologies). I argue that because of inductive risk, or the risk of error, non-epistemic values are required in science wherever non-epistemic consequences of error should be considered. I use examples from dioxin studies to illustrate how non-epistemic consequences of error can and should be considered (...) in the internal stages of science: choice of methodology, characterization of data, and interpretation of results. (shrink)

: In the last two decades, scientists, government officials, and science policy experts have expressed concerns about the increasing role of financial interests in research. Many believe that these interests are undermining research by causing bias and error, suppression of results, and even outright fraud. This paper seeks to shed some light on this view by (1) explicating the concept research bias, (2) describing some ways that financial interests can cause research biases, and (3) discussing some strategies for mitigating or (...) managing the influence of financial interests. These strategies include (a) using government funds to counter-balance privately funded research, (b) increasing public input into government funding decisions, (c) disclosing and managing conflicts of interest in research, (d) auditing data, (e) expanding access to data. Since it is neither possible nor desirable to eliminate financial interests from research, the wisest policy is to manage and counter-balance these interests for the good of science and society. (shrink)

The whole drug industry campaign for mood drugs in the 1950s was to broaden to absurd limits the definition of illness.... If the facts in these ads were not untruths, then their implications often were.1Sponsorship bias occurs when a funder of scientific research has a vested interest in what claims the research supports, which consequently shapes the research or the reporting of its results to align with that interest. This article examines the relationship between sponsorship bias and misleading claims, understood (...) as claims that are not necessarily false but which encourage those exposed to them to infer false conclusions. Such claims are important insofar as being common in examples of alleged sponsorship... (shrink)

Private companies provide by far the most funding for scientific research and development. Nevertheless, relatively little attention has been paid to the dynamics of industry‐funded research by philosophers of science. This paper addresses this gap by providing an overview of the major strengths and weaknesses of industry research funding, together with the existing recommendations for addressing the weaknesses. It is designed to provide a starting point for future philosophical work that explores the features of industry‐funded research, avenues for addressing concerns, (...) and strategies for making research funded by the private sector as fruitful as possible. (shrink)

Criticism plays an essential role in the growth of scientific knowledge. In some cases, however, criticism can have detrimental effects; for example, it can be used to ‘manufacture doubt’ for the purpose of impeding public policy making on issues such as tobacco consumption and greenhouse gas emissions (e.g., Oreskes & Conway 2010). In this paper, we build on previous work by Biddle and Leuschner (2015) who argue that criticism that meets certain conditions can be epistemically detrimental. We extend and refine (...) their account by arguing that such criticism can be epistemically corrupting—it can create social conditions that are conducive to the development of epistemic vice by agents operating within them. (shrink)

Claims that science should be more democratic than it is frequently arouse opposition. In this essay, I distinguish my own views about the democratization of science from the more ambitious theses defended by Paul Feyerabend. I argue that it is unlikely that the complexity of some scientific debates will allow for resolution according to the methodological principles of any formal confirmation theory, suggesting instead that major revolutions rest on conflicts of values. Yet these conflicts should not be dismissed as irresoluble.

The aim of this paper is to address the neglected but important problem of differentiating between epistemically beneficial and epistemically detrimental dissent. By “dissent,” we refer to the act of objecting to a particular conclusion, especially one that is widely held. While dissent in science can clearly be beneficial, there might be some instances of dissent that not only fail to contribute to scientific progress, but actually impede it. Potential examples of this include the tobacco industry’s funding of studies that (...) questioned the link between smoking and lung cancer, and the attempt by the petroleum industry and other groups to cast doubt upon the conclusion that human consumption of fossil fuels contributes to global climate change. The problem of distinguishing between good and bad dissent is important because of the growing tendency of some stakeholders to attempt to delay political action by ’manufacturing doubt’. Our discussion in this paper focuses on climate science. This field, in our view, is rife with instances of bad dissent. On the basis of our discussion of climate science, we articulate a set of sufficient conditions for epistemically problematic dissent in general, which we call “the inductive risk account of epistemically detrimental dissent.”. (shrink)

Epistemic trust is crucial for science. This article aims to identify the kinds of assumptions that are involved in epistemic trust as it is required for the successful operation of science as a collective epistemic enterprise. The relevant kind of reliance should involve working from the assumption that the epistemic endeavors of others are appropriately geared towards the truth, but the exact content of this assumption is more difficult to analyze than it might appear. The root of the problem is (...) that methodological decisions in science typically involve a complex trade-off between the reliability of positive results, the reliability of negative results, and the investigation's power. Which balance between these is the ‘correct’ one can only be determined in light of an evaluation of the consequences of all the different possible outcomes of the inquiry. What it means for the investigation to be ‘appropriately geared towards the truth’ thus depends on certain value judgments. I conclude that in the optimal case, trusting someone in her capacity as an information provider also involves a reliance on her having the right attitude towards the possible consequences of her epistemic work. 1 Introduction2 Epistemic Reliance within the Sciences3 Methodological Conventionalism4 Trust in Science5 Conclusions. (shrink)

Since skepticism is a familiar and critical feature of the realm of science, scientists generally presume that criticisms — even vigorous, cutthroat criticisms — are offered in the spirit of advancing scientific understanding and without hidden ...

The (recent, Bayesian) cognitive science literature on the Wason Task (WT) has been modeled largely after the (not-so-recent, Bayesian) philosophy of science literature on the Paradox of Confirmation (POC). In this paper, we apply some insights from more recent Bayesian approaches to the (POC) to analogous models of (WT). This involves, first, retracing the history of the (POC), and, then, re-examining the (WT) with these historico-philosophical insights in mind.

The question of the relationship of the making of value judgments in a typically ethical sense to the methods and procedures of science has been discussed in the literature at least to that point which e. e. cummings somewhere refers to as “The Mystical Moment of Dullness.” Nevertheless, albeit with some trepidation, I feel that something more may fruitfully be said on the subject.

Many have argued that allowing and encouraging public avenues for dissent and critical evaluation of scientific research is a necessary condition for promoting the objectivity of scientific communities and advancing scientific knowledge . The history of science reveals many cases where an existing scientific consensus was later shown to be wrong . Dissent plays a crucial role in uncovering potential problems and limitations of consensus views. Thus, many have argued that scientific communities ought to increase opportunities for dissenting views to (...) be heard and taken seriously. Such opportunities are necessary for both limiting the influence .. (shrink)