In empirical modeling, an important desiderata for deeming theoretical entities and processes as real is that they can be reproducible in a statistical sense. Current day crises regarding replicability in science intertwines with the question of how statistical methods link data to statistical and substantive theories and models. Different answers to this question have important methodological consequences for inference, which are intertwined with a contrast between the ontological commitments of the two types of models. The key to untangling them is (...) the realization that behind every substantive model there is a statistical model that pertains exclusively to the probabilistic assumptions imposed on the data. It is not that the methodology determines whether to be a realist about entities and processes in a substantive field. It is rather that the substantive and statistical models refer to different entities and processes, and therefore call for different criteria of adequacy. (shrink)

The main aim of this paper is to revisit the curve fitting problem using the reliability of inductive inference as a primary criterion for the ‘fittest' curve. Viewed from this perspective, it is argued that a crucial concern with the current framework for addressing the curve fitting problem is, on the one hand, the undue influence of the mathematical approximation perspective, and on the other, the insufficient attention paid to the statistical modeling aspects of the problem. Using goodness-of-fit as the (...) primary criterion for ‘best', the mathematical approximation perspective undermines the reliability of inference objective by giving rise to selection rules which pay insufficient attention to ‘accounting for the regularities in the data'. A more appropriate framework is offered by the error-statistical approach, where (i) statistical adequacy provides the criterion for assessing when a curve captures the regularities in the data adequately, and (ii) the relevant error probabilities can be used to assess the reliability of inductive inference. Broadly speaking, the fittest curve (statistically adequate) is not determined by the smallness if its residuals, tempered by simplicity or other pragmatic criteria, but by the nonsystematic (e.g. white noise) nature of its residuals. The advocated error-statistical arguments are illustrated by comparing the Kepler and Ptolemaic models on empirical grounds. ‡I am grateful to Deborah Mayo and Clark Glymour for many valuable suggestions and comments on an earlier draft of the paper; estimating the Ptolemaic model was the result of Glymour's prompting and encouragement. †To contact the author, please write to: Department of Economics, Virginia Tech 3019 Pamplin Hall (0316), Blacksburg, VA 24061; e-mail: [email protected]. (shrink)

The main objective of the paper is to propose a frequentist interpretation of probability in the context of model-based induction, anchored on the Strong Law of Large Numbers (SLLN) and justifiable on empirical grounds. It is argued that the prevailing views in philosophy of science concerning induction and the frequentist interpretation of probability are unduly influenced by enumerative induction, and the von Mises rendering, both of which are at odds with frequentist model-based induction that dominates current practice. The differences between (...) the two perspectives are brought out with a view to defend the model-based frequentist interpretation of probability against certain well-known charges, including [i] the circularity of its definition, [ii] its inability to assign ‘single event’ probabilities, and [iii] its reliance on ‘random samples’. It is argued that charges [i]–[ii] stem from misidentifying the frequentist ‘long-run’ with the von Mises collective. In contrast, the defining characteristic of the long-run metaphor associated with model-based induction is neither its temporal nor its physical dimension, but its repeatability (in principle); an attribute that renders it operational in practice. It is also argued that the notion of a statistical model can easily accommodate non-IID samples, rendering charge [iii] simply misinformed. (shrink)

: This paper explores the calibration of laboratory models in population genetics as an experimental strategy for justifying experimental results and claims based upon them following Franklin (1986, 1990) and Rudge (1996, 1998). The analysis provided undermines Coyne et al.'s (1997) critique of Wade and Goodnight's (1991) experimental study of Wright's (1931, 1932) Shifting Balance Theory. The essay concludes by further demonstrating how this analysis bears on Diamond's (1986) claims regarding the weakness of laboratory experiments as evidence, and further how (...) the calibration strategy fits within Lloyd's (1987, 1988) account of the confirmation of ecological and evolutionary models. (shrink)

Ian Hacking’s Representing and Intervening is often credited as being one of the first works to focus on the role of experimentation in philosophy of science, catalyzing a movement which is sometimes called the “philosophy of experiment” or “new experimentalism”. In the 1980s, a number of other movements and scholars also began focusing on the role of experimentation and instruments in science. Philosophical study of experimentation has thus seemed to be an invention of the 1980s whose central figure is Hacking. (...) This article aims to assess this historical claim, made by Hacking himself as well as others. It does so first by highlighting how a broader perspective on the history of philosophy reveals this invention narrative to be incorrect, since experimentation was a topic of interest for earlier philosophers. Secondly, the article evaluates a revision of this historical claim also made by some philosophers of experiment: the rediscovery narrative, which frames Hacking and others as having rediscovered the work of these earlier authors. This second narratives faces problems as well. Therefore we develop a third narrative which we call the contextualist narrative. Rather than considering experimentation in an essentialist manner as a fixed research object that is either present or not in the work of specific authors, experimentation should be addressed through a narrative that asks in what way it becomes a philosophical problem for certain authors and for what purpose. Such contextualization enables a repositioning of Hacking’s philosophy of experiment in relation to the specific debates in which he intervened, such as the realism-antirealism debate, the Science Wars and the debate on incommensurability. (shrink)

US testing of nuclear weapons has resulted in about 800,000 premature fatal cancers throughout the globe, and the nuclear tests of China, France, India, Russia, and the UK have added to this total. Surprisingly, however, these avoidable deaths have not received much attention, as compared, for example, to the smaller number of US fatalities on 9-11-01. This essay (1) surveys the methods and models used to assess effects of low-dose ionizing radiation from above-ground nuclear weapons tests and (2) explains some (...) of the epistemological and logical problems (with these methods and models) that have caused scientists to decide against health screening of the most likely test victims. It also (3) argues that, once the faulty presuppositions and question-begging frames about testing and screening are recognized, there are compelling arguments in favor of nuclear-test nations'' screening fallout victims, at least among their citizens. Finally, it (4) suggests that logically and epistemically flawed fallout studies/recommendations against screening are more like to occur when scientists adopt a Laudan-style comparativist rationality, rather than when they adopt a metascience more like that of Kuhn and others. (shrink)

This paper examines a twofold tension in Gerhard Schurz’s (2019) recent proposal to solve Hume’s problem of induction. Schurz concedes to the skeptic that there is no non-circular epistemic justification of the reliability of induction, but then argues for the optimality of meta-induction so that if any prediction method is reliable, then meta-induction is. There is a tension in this proposal between meta-induction and our inductive practice: Are we supposed to abandon our inductive practice in favor of meta-induction? Schurz claims (...) that given the actual way the world has been, the meta-inductive method supports the standard inductive method. There then arises a second tension: The challenges Schurz cites against the reliability of induction—such as the anti-inductive hypothesis and the state-uniform probability distribution—cast doubt on Schurz’s claim that meta-induction supports induction. The reasoning in this paper moves in the opposite direction: There are ways of answering these challenges in defense of Schurz’s claim that meta-induction supports induction, but these ways also point to ways of defending induction directly without the mediation of meta-induction. (shrink)

In this paper we argue that dissociative identity disorder (DID) is best interpreted as a causal model of a (possible) post-traumatic psychological process, as a mechanical model of an abnormal psychological condition. From this perspective we examine and criticize the evidential status of DID, and we demonstrate that there is really no good reason to believe that anyone has ever suffered from DID so understood. This is so because the proponents of DID violate basic methodological principles of good causal modeling. (...) When every ounce of your concentration is fixed upon blasting a winged pig out of the sky, you do not question its species' ontological status. James Morrow, City of Truth (1990). (shrink)

Résumé L’article évoque les bases de la méthode expérimentale en général et son adaptation à l’étude des phénomènes économiques tout en rendant compte des réflexions philosophiques récentes sur la nature des expériences en laboratoire. Il recense par ailleurs les principales procédures expérimentales qui caractérisent la bonne pratique des économistes – dont certaines témoignent d’une grande ingéniosité – en pointant en parallèle ce qui la sépare de celle plus ancienne des psychologues. La question des incitations financières y fait l’objet d’une attention (...) toute particulière. L’article souligne les immenses progrès réalisés en quelques décennies comme le caractère très professionnel de la démarche expérimentale, sans pour autant occulter les désaccords qui persistent entre certains expérimentalistes et l’existence de quelques questions « ouvertes » d’importance. A cet égard, l’article tente de faire le point sur l’un des questionnements méthodologiques qui, bien que central dans un argumentaire de défense contre la critique courante du caractère « artificiel » des expériences, n’est vraiment abordé de manière sérieuse que depuis peu : le problème de la « validité externe » des résultats expérimentaux, i.e. leur capacité à être généralisés en dehors du laboratoire. (shrink)

Whereas there is much discussion about Thomas Kuhn’s notion of methodological incommensurability and many have seen his ideas as an attempt to allow for rational disagreement in science, so far no serious analysis of how exactly Kuhn aims to account for rational disagreement has been proposed. This paper provides the first in-depth analysis of Kuhn’s account of rational disagreement in science—an account that can be seen as the most prominent attempt to allow for rational disagreement in science. Three things will (...) be shown: First, we find not one, but two accounts of rational disagreements in science in Kuhn’s writings: one stemming from methodological incommensurability and one stemming from Kuhn-underdetermination, which are not only fundamentally different—the first purports to explain how disagreeing scientists can nevertheless be rational, while the second attempts to show how rational scientists can nevertheless disagree—but appear to be incompatible with each other. Second, I will assess both accounts. Kuhn’s account from methodological incommensurability is not convincing since it cannot explain rational disagreement in science. Whereas, on the other hand, Kuhn’s account from Kuhn-underdetermination allows for rational disagreement, his argument why we should accept it is not convincing. Third, I present a tentative sketch of an alternative to Kuhn’s account that emphasizes the fallibility of epistemic justification in order to show that Kuhn’s argument founders. In sum, the paper shows that focusing not on the muchly debated consequences of methodological incommensurability, but on Kuhn’s treatment of rational disagreement gives new insight into the adequate interpretation of his thought as well as the cogency of his ideas. (shrink)

ABSTRACT When scientists seek further confirmation of their results, they often attempt to duplicate the results using diverse means. To the extent that they are successful in doing so, their results are said to be ‘robust’. This article investigates the logic of such ‘robustness analysis’. The most important and challenging question an account of RA can answer is what sense of evidential diversity is involved in RAs. I argue that prevailing formal explications of such diversity are unsatisfactory. I propose a (...) unified, explanatory account of diversity in RAs. The resulting account is, I argue, truer to actual cases of RA in science; moreover, this account affords us a helpful new foothold on the logic undergirding RAs. 1Robustness Analysis in Science 2RA Diversity and Independence 2.1Unconditional probabilistic independence 2.2Reliability independence 2.3Confirmational and conditional independence 2.4Partial independence? 3Robustness Analysis as Explanatory Reasoning 3.1Explanatorily discriminating means of detection 3.2The logic of robustness analysis 3.2.1Mutually exclusive competitors 3.2.2Consistent epistemic competitors 4Conclusion Appendix. (shrink)

: This paper is concerned with the claim that epistemic terms and categories are historical entities. The starting point is the observation that recent attempts at historical studies of epistemic terms fail to bridge the gap between history and philosophy proper. I examine whether, and how, it is possible to forge a closer link between historical and philosophical aspects of conceptual analysis. The paper explores possible links by analyzing aspects of the concept of error. A "pragmatic" and a "mentalist" notion (...) of error are identified in current philosophical studies of error: according to the latter, errors can be ascribed only to mental operations, according to the former, errors can also be ascribed to things and processes. The paper then draws on historical accounts of optical instruments to highlight certain presuppositions and implications of these two uses of the term. Contextual features and trans-contextual structures of the notion of error are distinguished. In conclusion, I argue that an intimate link between history and philosophy of science can be forged by an analysis ofthe development of conceptual arrangements which allows for trans- contextual structural aspects while drawing attention to the contextual epistemological and scientific conditions of their re-arrangement. (shrink)

: Bayesians and error statisticians have relied heavily upon examples from physics in developing their accounts of scientific inference. The present essay demonstrates it is possible to analyze H.B.D. Kettlewell's classic study of natural selection from Deborah Mayo's error statistical point of view (Mayo 1996). A comparison with a previous analysis of this episode from a Bayesian perspective (Rudge 1998) reveals that the error statistical account makes better sense of investigations such as Kettlewell's because it clarifies how core elements in (...) the design of experiments are used to minimize erroneous inferences rather than dwelling on whether the strategies used are reasonable. (shrink)

: Most work done in philosophy of experiment has focused on experiments taken from the domain of physics. The present essay tests whether Allan Franklin's (1984, 1986, 1989, 1990) philosophy of experiment developed in the context of high energy physics can be extended to include examples from evolutionary biology, such as H. B. D. Kettlewell's (1955, 1956, 1958) famous studies of industrial melanism in the peppered moth, Biston betularia. The analysis demonstrates that many of the techniques used by evolutionary biologists (...) exemplify the strategies Franklin lists, and identifies an additional strategy that can likewise be justified by appeal to Bayes's Theorem. (shrink)

Kerr coined the term ‘HARKing’ to refer to the practice of ‘hypothesizing after the results are known’. This questionable research practice has received increased attention in recent years because it is thought to have contributed to low replication rates in science. The present article discusses the concept of HARKing from a philosophical standpoint and then undertakes a critical review of Kerr’s twelve potential costs of HARKing. It is argued that these potential costs are either misconceived, misattributed to HARKing, lacking evidence, (...) or that they do not take into account pre- and post-publication peer review and public availability to research materials and data. It is concluded that it is premature to conclude that HARKing has led to low replication rates. (shrink)

The enduring replication crisis in many scientific disciplines casts doubt on the ability of science to estimate effect sizes accurately, and in a wider sense, to self-correct its findings and to produce reliable knowledge. We investigate the merits of a particular countermeasure—replacing null hypothesis significance testing with Bayesian inference—in the context of the meta-analytic aggregation of effect sizes. In particular, we elaborate on the advantages of this Bayesian reform proposal under conditions of publication bias and other methodological imperfections that are (...) typical of experimental research in the behavioral sciences. Moving to Bayesian statistics would not solve the replication crisis single-handedly. However, the move would eliminate important sources of effect size overestimation for the conditions we study. (shrink)

Advocates of the self-corrective thesis argue that scientific method will refute false theories and find closer approximations to the truth in the long run. I discuss a contemporary interpretation of this thesis in terms of frequentist statistics in the context of the behavioral sciences. First, I identify experimental replications and systematic aggregation of evidence (meta-analysis) as the self-corrective mechanism. Then, I present a computer simulation study of scientific communities that implement this mechanism to argue that frequentist statistics may converge upon (...) a correct estimate or not depending on the social structure of the community that uses it. Based on this study, I argue that methodological explanations of the “replicability crisis” in psychology are limited and propose an alternative explanation in terms of biases. Finally, I conclude suggesting that scientific self-correction should be understood as an interaction effect between inference methods and social structures. (shrink)

This paper is an extended review of the book Error and Inference, edited by Deborah Mayo and Aris Spanos, about their frequentist and philosophical perspective on testing of hypothesis and on the criticisms of alternatives like the Bayesian approach.

Biased research occurs frequently in the sciences. In this paper, I will focus on one particular kind of biased research: research that is subject to sponsorship bias. I will address the following epistemological question: what precisely is epistemically wrong with biased research of this kind? I will defend the evidential account of epistemic wrongness: that is, research affected by sponsorship bias is epistemically wrong if and only if the researchers in question make false claims about the evidential support of some (...) hypothesis H by data E. I will argue that the evidential account captures the epistemic wrongness of three paradigmatic types of sponsorship bias. (shrink)

This paper reviews all major theories of evidence such as the Bayesian theory, hypothetico-deductivism, satisfaction theories, error-statistics, Achinstein's explanationist theory and Cartwright's argument theory. All these theories fail to take adequate account of the context in which a hypothesis is established and used. It is argued that the context of an inquiry determines important facts about what evidence is, and how much and what kind has to be collected to establish a hypothesis for a given purpose.

One way to make philosophy of science more socially relevant is to attend to specific scientific practises that affect society to a great extent. One such practise is biomedical research. This paper looks at contemporary U.S. biomedical research in particular and argues that it suffers from important epistemic, moral and socioeconomic failings. It then discusses and criticises existing approaches to improve on the status quo, most prominently by Thomas Pogge (a political philosopher), Joseph Stiglitz (a Nobel-prize winning economist) and James (...) Robert Brown (a philosopher of science). Finally, it sketches an alternative proposal and argues for its superiority. The proposal has four components: changing the intellectual property regime; instituting independent clinical research; aligning innovators' and patients' interests; and enacting additional regulation. (shrink)

Two approaches to evidential reasoning compete in the biomedical and social sciences: the experimental and the pragmatist. Whereas experimentalism has received considerable philosophical analysis and support since the times of Bacon and Mill, pragmatism about evidence has been neither articulated nor defended. The overall aim is to fill this gap and develop a theory that articulates the latter. The main ideas of the theory will be illustrated and supported by a case study on the smoking/lung cancer controversy in the 1950s.

: For philosophers of science interested in elucidating the social character of science, an important question concerns the manner in which and degree to which the objectivity of scientific knowledge is socially constituted. We address this broad question by focusing specifically on philosophical theories of evidence. To get at the social character of evidence, we take an interdisciplinary approach informed by categories from argumentation studies. We then test these categories by exploring their applicability to a case study from high-energy physics. (...) Our central claim is that normative philosophy of science must move beyond abstract theories of justification, confirmation, or evidence conceived impersonally and incorporate a theoretical perspective that includes dialogical elements, either as adjuncts to impersonal theories of evidence or as intrinsic to the cogency of scientific argumentation. (shrink)

The paper proposes a practical answer to Duhem's problem within the framework of experimental psychology. First, this problem is briefly discussed; second, two studies in psychology are presented illustrating how theories are tested. Thirdly, based on the foregoing, an approach called the “Empirical Reasoning” is developed and justified. It is shown that the ER approach can successfully cope with Duhem's problem. Finally, the ER approach and the Error Statistics approach of Mayo are critically compared with regard to Duhem's problem.

The debates between Bayesian, frequentist, and other methodologies of statistics have tended to focus on conceptual justifications, sociological arguments, or mathematical proofs of their long run properties. Both Bayesian statistics and frequentist (“classical”) statistics have strong cases on these grounds. In this article, we instead approach the debates in the “Statistics Wars” from a largely unexplored angle: simulations of different methodologies’ performance in the short to medium run. We conducted a large number of simulations using a straightforward decision problem based (...) around tossing a coin with unknown bias and then placing bets. In this simulation, we programmed four players, inspired by Bayesian statistics, frequentist statistics, Jon Williamson’s version of Objective Bayesianism, and a player who simply extrapolates from observed frequencies to general frequencies. The last player functions as a benchmark: a statistical methodology should at least outperform a crude form of induction. We focused on the performance of these methodologies in guiding the players towards good decisions. Unlike an earlier simulation study of this type, we found no systematic difference in performance between the Bayesian and frequentist players, provided the Bayesian used a flat prior and the frequentist used a low confidence level. Unlike that study, we were able to use Big Data methods to mitigate problems of random error in the simulation results. The Williamsonian player, who is a novel element of our study, also had no systematic differences in their performance, provided that they use a low confidence level. These players performed similarly even in the very short run, when players were making different decisions. Our study indicates that all three methodologies should be taken seriously by philosophers and practitioners of statistics. However, the frequentist and Williamsonian performed poorly when their confidence levels were high, and the Bayesian was surprisingly harmed by biased priors, providing some unexpected lessons for these methodologies when facing this type of decision problem. (shrink)

We provide a full characterization of computational error states for information systems. The class of errors considered is general enough to include human rational processes, logical reasoning, scientific progress and data processing in some functional programming languages. The aim is to reach a full taxonomy of error states by analysing the recovery and processing of data. We conclude by presenting machine-readable checking and resolve algorithms.

The 1980s, it is often claimed, was the decade when experimentation finally became a philosophical topic. This was the responsibility, the claim continues, of one particular movement within philosophy of science, called “new experimentalism.” The aim of this article is to complicate this historical narrative. We argue that in the 1980s, the study of experimentation was carried out not by one movement with one particular aim but rather in a diverse and open-ended way by people with different aims and backgrounds. (...) We then argue that from the late 1990s onward, this diversity disappeared and made room for disciplinary divisions—questions concerning experimentation became philosophical, others sociological, and so on. The reason for this, we claim, was that science and technology studies, philosophy of technology, and philosophy of science took over aspects of the 1980s study of experimentation. In this way, we argue, these elements became institutionalized, whereas others were forgotten. The importance of this process of institutionalization is illustrated by means of a discussion of other, similar approaches to the philosophy of experimentation that have not been able to ensure continuity because they did not find an institutional home. (shrink)

Philosophers of science have often favoured reductive approaches to how-possibly explanation. This article identifies three alternative conceptions making how-possibly explanation an interesting phenomenon in its own right. The first variety approaches “how possibly X?” by showing that X is not epistemically impossible. This can sometimes be achieved by removing misunderstandings concerning the implications of one’s current belief system but involves characteristically a modification of this belief system so that acceptance of X does not result in contradiction. The second variety offers (...) a potential how-explanation of X. It is usually followed by a range of further potential how-explanations of the same phenomenon. In recent literature the factual claims implied by the second variety have been downplayed whereas the heuristic role of mapping the space of conceptual possibilities has been emphasized. I will focus especially on this truth-bracketing sense of potentiality when looking closer at the second variety in the paper. The third variety has attracted less interest. It presents a partial how-explanation of X. Typically it aims to establish the existence of a mechanism by which X could be and was generated. The third conception stands out as the natural alternative for the advocate of ontic how-possibly explanations. This article transfers Salmon’s view that explanation-concepts can be broadly divided into epistemic, modal, and ontic to the context of how-possibly explanations. Moreover, it is argued that each of the three above-mentioned varieties of how-possibly explanation occurs in science. To recognize this may be especially relevant for philosophers. We are often misled by the promises of various why-explanation accounts, and seem to have forgotten nearly everything about the diversity of how-possibly explanations. (shrink)

I examine the role of inference from experiment in theory building. What are the options open to the scientific community when faced with an experimental result that appears to be in conflict with accepted theory? I distinguish, in Laudan's (1977), Nickels's (1981), and Franklin's (1993) sense, between the context of pursuit and the context of justification of a scientific theory. Making this distinction allows for a productive middle position between epistemic realism and constructivism. The decision to pursue a new or (...) a revised theory in response to the new evidence may not be fully rationally determined. Nonetheless, it is possible to distinguish the question of whether there is reason to pursue a theory from the question of whether that theory, once it has been pursued over time, solves a problem of interest to science. I argue that, in this context, there is a solid way to distinguish between the contexts of pursuit and of justification, on the basis of a theory's evidential support and problem-solving ability. (shrink)

Lloyd (2009) contends that climate models are confirmed by various instances of fit between their output and observational data. The present paper argues that what these instances of fit might confirm are not climate models themselves, but rather hypotheses about the adequacy of climate models for particular purposes. This required shift in thinking—from confirming climate models to confirming their adequacy-for-purpose—may sound trivial, but it is shown to complicate the evaluation of climate models considerably, both in principle and in practice.

Allan Franklin has identified a number of strategies that scientists use to build confidence in experimental results. This paper shows that Franklin's strategies have direct analogues in the context of computer simulation and then suggests that one of his strategies—the so-called 'Sherlock Holmes' strategy—deserves a privileged place within the epistemologies of experiment and simulation. In particular, it is argued that while the successful application of even several of Franklin's other strategies (or their analogues in simulation) may not be sufficient for (...) justified belief in results, the successful application of a slightly elaborated version of the Sherlock Holmes strategy is sufficient. (shrink)

After showing how Deborah Mayo’s error-statistical philosophy of science might be applied to address important questions about the evidential status of computer simulation results, I argue that an error-statistical perspective offers an interesting new way of thinking about computer simulation models and has the potential to significantly improve the practice of simulation model evaluation. Though intended primarily as a contribution to the epistemology of simulation, the analysis also serves to fill in details of Mayo’s epistemology of experiment.

Clinical trials play a prominent role today in medicine, but are not without controversy. These issues start from the day physicians begin their specialization process in medical school and continues onto their day-to-day practice as attendings with referral patterns and resulting financial incentives. This combined with the lack of training in basic issues of epistemology and statistics, allows poor interpretations of clinical trials to reign free. A proposal to integrate the notion of severity to help remedy these issues are made (...) by ensuring that studies are tested “As Severe As Reasonably Possible” will put P-values, multiplicity adjustments, and model checking in their rightful place. In light of this, multiple clinical trials are examined with an in-depth analysis of erroneous statistical inferences taken from phase III randomized trials, non-inferiority, early phase, as well as observational studies. Severe testing in the setting of medicine will help with alleviate some of the issues with the current misinterpretation of clinical studies. (shrink)

Amedeo Giorgi has published a review article devoted to Phenomenology as Qualitative Research: A Critical Analysis of Meaning Attribution. However, anyone reading this article, but unfamiliar with the book, will get a distorted view of what it is about, whom it is addressed to, what it seeks to achieve and how it goes about presenting its arguments. Not mildly distorted, in need of the odd correction here and there, but systematically misrepresented. The article is a study in misreading. Giorgi misreads (...) the book's mise en scène; he misreads its narrative arc; he misreads individual arguments; he misreads short, simple passages; he misreads the philosophy of the science literature; he misreads his own data; he misreads the title; he misreads the blurb; he misreads the acknowledgements. In addition, there are serious failures of scholarship (ironically, he demonstrates how unacquainted he is with the relevant literature at the very moment he is accusing me of being ill‐informed). In this reply, I provide several examples of these errors, but my primary aim is to understand why Giorgi's misreading is as ubiquitous as it is. To this end, I explain his mistakes by reference to the hermetic epistemology within which he is confined. (shrink)

Phenomenology as rhetoric The literature on ‘nursing phenomenology’ is driven by a range of ontological and epistemological considerations, intended to distance it from conventionally scientific approaches. However, this paper examines a series of discrepancies between phenomenological rhetoric and phenomenological practice. The rhetoric celebrates perceptions and experience; but the concluding moment of a research report almost always makes implicit claims about reality. The rhetoric insists on uniquely personal meanings; but the practice offers blank, anonymous abstractions. The rhetoric invites us to believe (...) that knowing is subjective and involved, but at the same time it recommends a technique (bracketing) which can only represent a crude, and entirely misconceived, gesture towards objectivity. Finally, the rhetoric claims that generalisation is beside the point; but the majority of researchers generalise anyway. In quietly ignoring their own rhetoric, ‘phenomenologists’ appropriate scientific prerogatives illegitimately. For their methods do not entitle them to lay claim to anything resembling ‘objectivity’, or generalisability, or ‘reality’, or theoretical abstraction. Like other researchers, they want to talk in generalisable terms about reality; they want to be objective, they want to do theory. But they are saddled with a philosophy that is disabling, because it says they can only talk about perceptions, and meanings, and uniqueness. (shrink)

This paper evaluates attempts to defend established concepts of expertise and clinical judgement against the incursions of evidence‐based practice. Two related arguments are considered. The first suggests that standard accounts of evidence‐based practice imply an overly narrow view of ‘evidence’, and that a more inclusive concept, incorporating ‘patterns of knowing’ not recognised by the familiar evidence hierarchies, should be adopted. The second suggests that statistical generalisations cannot be applied non‐problematically to individual patients in specific contexts, and points out that this (...) is why we need clinical judgement. In evaluating the first argument, I propose a criterion for what counts as evidence. It is a minimalist criterion but the ‘patterns of knowing’, referred to in the literature, still fail to meet it. In evaluating the second argument, I will outline the powerful empirical reasons we have for thinking that decisions based on research evidence are usually better than decisions based on clinical judgement; and show that current efforts to rehabilitate clinical judgement seriously underestimate the strength of these reasons. By way of conclusion, I will sketch the ways in which the concept of expertise will have to be modified if we accept evidence‐based practice as a template for health‐care. (shrink)

Psychological research has shown that cognitive illusions, of which visual illusions are just a special case, are systematic and pervasive, raising epistemological questions about how error in all forms of research can be identified and eliminated. The quantitative sciences make use of statistical techniques for this purpose, but it is not clear what the qualitative equivalent is, particularly in view of widespread scepticism about validity and objectivity. I argue that, in the light of cognitive psychology, the ‘error question’ cannot be (...) dismissed as a positivist obsession, and that the concepts of truth and objectivity are unavoidable. However, they constitute only a ‘minimal realism’, which does not necessarily bring a commitment to ‘absolute’ truth, certainty, correspondence, causation, reductionism, or universal laws in its wake. The assumption that it does reflects a misreading of positivism and, ironically, precipitates a ‘crisis of legitimation and representation’, as described by constructivist authors. (shrink)

Psychological research has shown that cognitive illusions, of which visual illusions are just a special case, are systematic and pervasive, raising epistemological questions about how error in all forms of research can be identified and eliminated. The quantitative sciences make use of statistical techniques for this purpose, but it is not clear what the qualitative equivalent is, particularly in view of widespread scepticism about validity and objectivity. I argue that, in the light of cognitive psychology, the ‘error question’ cannot be (...) dismissed as a positivist obsession, and that the concepts of truth and objectivity are unavoidable. However, they constitute only a ‘minimal realism’, which does not necessarily bring a commitment to ‘absolute’ truth, certainty, correspondence, causation, reductionism, or universal laws in its wake. The assumption that it does reflects a misreading of positivism and, ironically, precipitates a ‘crisis of legitimation and representation’, as described by constructivist authors. (shrink)

The remarkable transition from helpless infant to sophisticatedfive-year-old has long captured the attention of scholars interested inthe discovery of knowledge. To explain these achievements, developmentalpsychologists often compare children's discovery procedures to those ofprofessional scientists. For the child to be qualified as a ``littlescientist'', however, intellectual development must be shown to derivefrom rational hypothesis selection in the face of evidence. In thepresent paper we focus on one dimension of rational theory-choice,namely, the relation between hypothesis confirmation and evidencediversity. Psychological research suggests cultural (...) variability inappreciating evidence diversity and lack of such appreciation by youngchildren. Before reaching conclusions about the ``little scientist''thesis, however, it is essential to normatively analyze the diversityissue. We undertake such an analysis within a Bayesianperspective. (shrink)

I examine error diagnosis (model-model disagreement) in climate model intercomparisons including its difficulties, fruitful examples, and prospects for streamlining error diagnosis. I suggest that features of climate model intercomparisons pose a more significant challenge for error diagnosis than do features of individual model construction and complexity. Such features of intercomparisons include, e.g., the number of models involved, how models from different institutions interrelate, and what scientists know about each model. By considering numerous examples in the climate modeling literature, I distill (...) general strategies (e.g., employing physical reasoning and using dimension reduction techniques) used to diagnose model error. Based on these examples, I argue that an error repertoire could be beneficial for improving error diagnosis in climate modeling, although constructing one faces several difficulties. Finally, I suggest that the practice of error diagnosis demonstrates that scientists have a tacit-yet-working understanding of their models which has been under-appreciated by some philosophers. (shrink)

The history of statistics is filled with many controversies, in which the prime focus has been the difference in the “interpretation of probability” between Fre- quentist and Bayesian theories. Many philosophical arguments have been elabo- rated to examine the problems of both theories based on this dichotomized view of statistics, including the well-known stopping-rule problem and the catch-all hy- pothesis problem. However, there are also several “hybrid” approaches in theory, practice, and philosophical analysis. This poses many fundamental questions. This paper (...) reviews three cases and argues that the interpretation problem of probabil- ity is insufficient to begin a philosophical analysis of the current issues in the field of statistics. A novel viewpoint is proposed to examine the relationship between the stopping-rule problem and the catch-all hypothesis problem. (shrink)

Contrary to formal theories of induction, I argue that there are no universal inductive inference schemas. The inductive inferences of science are grounded in matters of fact that hold only in particular domains, so that all inductive inference is local. Some are so localized as to defy familiar characterization. Since inductive inference schemas are underwritten by facts, we can assess and control the inductive risk taken in an induction by investigating the warrant for its underwriting facts. In learning more facts, (...) we extend our inductive reach by supplying more localized inductive inference schemes. Since a material theory no longer separates the factual and schematic parts of an induction, it proves not to be vulnerable to Hume's problem of the justification of induction. (shrink)

A complete calculus of inductive inference captures the totality of facts about inductive support within some domain of propositions as relations or theorems within the calculus. It is demonstrated that there can be no complete, non-trivial calculus of inductive inference.