Theoretical arguments and empirical investigations indicate that a high proportion of published findings do not replicate and are likely false. The current position paper provides a broad perspective on scientific error, which may lead to replication failures. This broad perspective focuses on reform history and on opportunities for future reform. We organize our perspective along four main themes: institutional reform, methodological reform, statistical reform and publishing reform. For each theme, we illustrate potential errors by narrating the story of a fictional (...) researcher during the research cycle. We discuss future opportunities for reform. The resulting agenda provides a resource to usher in an era that is marked by a research culture that is less error-prone and a scientific publication landscape with fewer spurious findings. (shrink)

According to the variety of evidence thesis items of evidence from independent lines of investigation are more confirmatory, ceteris paribus, than, for example, replications of analogous studies. This thesis is known to fail (Bovens and Hartmann; Claveau). However, the results obtained by Bovens and Hartmann only concern instruments whose evidence is either fully random or perfectly reliable; instead, for Claveau, unreliability is modelled as deterministic bias. In both cases, the unreliable instrument delivers totally irrelevant information. We present a model that (...) formalizes both reliability and unreliability differently. Our instruments either are reliable, but affected by random error, or are biased but not deterministically so.Bovens and Hartmann’s results are counter-intuitive in that in their model a long series of consistent reports from the same instrument does not raise suspicion of ‘too-good-to-be-true’ evidence. This happens precisely because they contemplate neither the role of systematic bias, nor unavoidable random error of reliable instruments. In our model, the variety of evidence thesis fails as well, but the area of failure is considerably smaller than for Bovens and Hartmann and Claveau, and holds for (the majority of) realistic cases (that is, where biased instruments are very biased). The essential mechanism that triggers variety of evidence thesis failure is the rate of false to true positives for the two kinds of instruments. Our emphasis is on modelling beliefs about sources of knowledge and their role in hypothesis confirmation in interaction with dimensions of evidence, such as variety and consistency. (shrink)

Assessment of error and uncertainty is a vital component of both natural and social science. This edited volume presents case studies of research practices across a wide spectrum of scientific fields. It compares methodologies and presents the ingredients needed for an overarching framework applicable to all.

Non-classical science gives a very specific answer to the question of scientific errors and their epistemological value. But for all the specificity of this answer, it casts light on a problem that remains with us century after century, the historically constant problem of truth and error—one of the most fundamental problems of knowledge. At first sight, these two poles have always stood opposite each other, like good and evil, beauty and ugliness. But moral and aesthetic theories have long since left (...) behind this initial conception, and shown that the two polar concepts are in fact inseparable. As regards truth and error, their indivisibility has only become apparent in the context of non-classical science. Truth has ceased to be the absolute contradiction of error. Contemporary science finds it to be something relative, inseparable from its opposite pole. But the non-classical, retrospective approach, a re-evaluation of the past of science in the light of its present, and indeed even more in the light of its prognosis, its future, allows one to see that scientific truths that are adequate for the object of knowledge always reflect only one side of the objective world, a world which has an infinite number of sides. The irreversible process that leads from relative error to relative truth has an absolute character: an irreversible process of generalization, concretization and complication of a Weltanschauung that more accurately reflects the infinite complexity of life. (shrink)

When Kangas suggested in 1986 that wildlife reserve designs could be much smaller than previously thought, community ecologists attacked his views on methodological grounds (island biogeographical theory is beset with uncertainties) and on conservation grounds (Kangas seemed to encourage deforestation and extinction). Kangas' defenders, like Simberloff, argued that in a situation of biological uncertainty (the degree/type of deforestation-induced extinction), scientists ought to follow the epistemologically conservative course and risk type-II error (the risk of not rejecting a null hypothesis that is (...) false), rather than type-I error. (This is the risk of rejecting a null hypothesis that is true). Kangas' opponents, like Noss, argued that, in a situation of scientific uncertainty, scientists ought to risk type-I, rather than type-II, error. This essay argues that there are different types of rationality appropriate to science and applied science and, therefore, in cases of applied science (like conservation biology), the more conservative course of action is for scientists to risk type-I error. The essay argues further that, on grounds of scientific rationality, Kangas, Simberloff, and others were correct in risking type-II error, but that, on grounds of decision-theoretic rationality, Noss, Waide, and others were correct in risking type-I error. (shrink)

Error is protean, ubiquitous and crucial in scientific process. In this paper it is argued that understanding scientific process requires what is currently absent: an adaptable, context-sensitive functional role for error in science that naturally harnesses error identification and avoidance to positive, success-driven, science. This paper develops a new account of scientific process of this sort, error and success driving Self-Directed Anticipative Learning (SDAL) cycling, using a recent re-analysis of ape-language research as test example. The example shows the limitations of (...) other accounts of error, in particular Mayo’s (Error and the growth of experimental knowledge, 1996) error-statistical approach, and SDAL cycling shows how they can be fruitfully contextualised. (shrink)

This essay is concerned with the epistemic roles of error in scientific practice. Usually, error is regarded as something negative, as an impediment or obstacle for the advancement of science. However, we also frequently say that we are learning from error. This common expression suggests that the role of error is not—at least not always—negative but that errors can make a fruitful contribution to the scientific enterprise. My paper explores the latter possibility. Can errors play an epistemically productive role in (...) scientific research? The paper begins with a review of several twentieth-century approaches to error and the various agendas behind them. It is shown that only very few scholars have considered whether errors can be productive. The main part of the paper examines a concrete debate in early nineteenth-century microscopy and analyses how the microscopists coped with the problem of error. Drawing on this material, the article offers some terminological clarifications of the common notion ‘learning from error’. The conclusion argues that error can indeed play epistemically productive roles in scientific practice.Keywords: Error; Nineteenth-century microscopic anatomy. (shrink)

According to the Variety of Evidence Thesis items of evidence from independent lines of investigation are more confirmatory, ceteris paribus, than e.g. replications of analogous studies. This thesis is known to fail Bovens and Hartmann, Claveau. How- ever, the results obtained by the former only concern instruments whose evidence is either fully random or perfectly reliable; instead in Claveau, unreliability is modelled as deterministic bias. In both cases, the unreliable instrument delivers totally irrelevant information. We present a model which formalises (...) both reliability, and unreliability, differently. Our instruments are either reliable, but affected by random error, or they are biased but not deterministically so. Bovens and Hartmann’s results are counter-intuitive in that in their model a long series of consistent reports from the same instrument does not raise suspicion of “too-good-to- be-true” evidence. This happens precisely because they neither contemplate the role of systematic bias, nor unavoidable random error of reliable instruments. In our model the Variety of Evidence Thesis fails as well, but the area of failure is considerably smaller than for Bovens and Hartmann, Claveau and holds for realistic cases. The essential mechanism which triggers VET failure is the rate of false to true positives for the two kinds of instruments. Our emphasis is on modelling beliefs about sources of knowledge and their role in hypothesis confirmation in interaction with dimensions of evidence, such as variety and consistency. (shrink)

ABSTRACTComparative effectiveness studies, referred to here as “usual-care” trials, seek to compare current medical practices for the same medical condition. Such studies are presumed to be safe and involve only minimal risks. However, that presumption may be flawed if the trial design contains “unusual” care, resulting in potential risks to subjects and inaccurately informed consent. Three case studies described here did not rely on clinical evidence to ascertain contemporaneous practice. As a result, the investigators drew inaccurate conclusions, misinformed research participants, (...) and subjects’ safety was compromised. Before approving usual-care protocols, IRBs and scientific review committees should evaluate the quality and completeness of information documenting usual-care practices. Guidance from governmental oversight agencies regarding evidence-based documentation of current clinical practice could prevent similar occurrences in future usual-care trials. Accurate information is necessary to ensure that trials comply with government regulations that require minimizing research risks to subjects and accurate informed consent documents. (shrink)

Three new versions of Wason’s 2-4-6 rule discovery task incorporating error rates or feedback of uncertainty reduction, inspired by the error-statistical account in philosophy of science, were employed. In experiments 1 and 2, participants were instructed that some experimenter feedback would be erroneous (control was original 2-4-6 without error). The results showed that performance was impaired when there was probabilistic error. In experiment 3, participants were given uncertainty reduction feedback as they generated different number triples and the negative effects of (...) probabilistic error were not observed. These findings are informative not only about rule discovery tasks in general but also about contexts of inference under uncertainty. (shrink)

We present a taxonomy of errors in the scientific literature and an account of how the errors are distributed over the categories. We have developed the taxonomy by studying substantial errors in the scientific literature as described in retraction notices published in the journal Science over the past 35 years. We then examine how the sorts of errors that lead to retracted papers can be prevented and detected, considering the perspective of collaborating scientists, journal editors and referees, and readers of (...) the published articles. (shrink)

We provide examples of the extent and nature of environmental and human health problems and show why in the United States prevailing scientific and legal burden of proof requirements usually cannot be met because of the pervasiveness of scientific uncertainty. We also provide examples of how may assumptions, judgments, evaluations, and inferences in scientific methods are value-laden and that when this is not recognized results of studies will appear to be more factual and value-neutral than warranted. Further, we show that (...) there is a "tension" between the use of the 95 percent confidence rule as a normative basis to reduce speculation in scientific knowledge and other public policy and moral concerns embodied by the adoption of a precautionary principle. Finally, although there is no precise agreement regarding what a precautionary principle might entail, we make several recommendations regarding the placement of the burden of proof and the standard of proof that ought to be required in environmental and human health matters. (shrink)

In general, in our ordinary life, we manage to make the difference between “strange” behavior and error or extravagant beliefs. The question is here to know how we do so, and against what background. There are also specialized contexts for evaluating whether certain types of behavior or discourse are normal or abnormal: courts of law and psychiatric hospitals are two examples. In these contexts, judgments are formed against a background of technical or scientific knowledge, but they also result from epistemic (...) means of evaluation that are similar to habitual ones. The paper seeks to highlight this similarity with respect to recognizing mental disturbance. Starting from Wittgenstein’s On Certainty, it attempts at extending it by drawing on notions of reciprocal perspectives and of judgments of incongruity. It documents its investigation by analyzing sequences from Malek Bensmail’s documentary, Aliénations, which examines the treatment of mental suffering in contemporary Algeria. (shrink)

Error theorists about color argue that our ordinary judgments ascribing color to material objects are all false. The error theorist proposes that everything that is so, including the fact that material objects appear to us to have color, can best be explained without ever attributing color to objects (for instance, by appealing to surface reflectance properties, the nature of light, the neurophysiology of perceivers, etc.). The appeal of this view stems in significant part from the prevalent thought that such explanations (...) are strongly suggested by our present scientific conception of the world. Recently, however, Barry Stroud has argued that error theorists cannot successfully even acknowledge, let alone explain, all of the facts that error theorists must acknowledge. In this paper, I shall raise an objection to Stroud's argument. I shall conclude by mentioning one way in which we might develop Stroud's general strategy with more success. (shrink)

Software-intensive systems are ubiquitous in the industrialized world. The reliability of software has implications for how we understand scientific knowledge produced using software-intensive systems and for our understanding of the ethical and political status of technology. The reliability of a software system is largely determined by the distribution of errors and by the consequences of those errors in the usage of that system. We select a taxonomy of software error types from the literature on empirically observed software errors and compare (...) that taxonomy to Giuseppe Primiero’s Minds and Machines 24: 249–273, (2014) taxonomy of error in information systems. Because Primiero’s taxonomy is articulated in terms of a coherent, explicit model of computation and is more fine-grained than the empirical taxonomy we select, we might expect Primiero’s taxonomy to provide insights into how to reduce the frequency of software error better than the empirical taxonomy. Whether using one software error taxonomy can help to reduce the frequency of software errors better than another taxonomy is ultimately an empirical question. (shrink)

We argue for a naturalistic account for appraising scientific methods that carries non-trivial normative force. We develop our approach by comparison with Laudan’s (American Philosophical Quarterly 24:19–31, 1987, Philosophy of Science 57:20–33, 1990) “normative naturalism” based on correlating means (various scientific methods) with ends (e.g., reliability). We argue that such a meta-methodology based on means–ends correlations is unreliable and cannot achieve its normative goals. We suggest another approach for meta-methodology based on a conglomeration of tools and strategies (from statistical modeling, (...) experimental design, and related fields) that affords forward looking procedures for learning from error and for controlling error. The resulting “error statistical” appraisal is empirical—methods are appraised by examining their capacities to control error. At the same time, this account is normative, in that the strategies that pass muster are claims about how actually to proceed in given contexts to reach reliable inferences from limited data. (shrink)

Although both philosophers and scientists are interested in how to obtain reliable knowledge in the face of error, there is a gap between their perspectives that has been an obstacle to progress. By means of a series of exchanges between the editors and leaders from the philosophy of science, statistics and economics, this volume offers a cumulative introduction connecting problems of traditional philosophy of science to problems of inference in statistical and empirical modelling practice. Philosophers of science and scientific practitioners (...) are challenged to reevaluate the assumptions of their own theories - philosophical or methodological. Practitioners may better appreciate the foundational issues around which their questions revolve and thereby become better 'applied philosophers'. Conversely, new avenues emerge for finally solving recalcitrant philosophical problems of induction, explanation and theory testing. (shrink)

This paper argues, first, that recent studies of experimentation, most notably by Deborah Mayo, provide the conceptual resources to describe scientific discovery's early stages as error-probing processes. Second, it shows that this description yields greater understanding of those early stages, including the challenges that they pose, the research strategies associated with them, and their influence on the rest of the discovery process. Throughout, the paper examines the phenomenon of "chemical hormesis" (i.e., anomalous low-dose effects from toxic chemicals) as a case (...) study that is important not only for the biological sciences but also for contemporary public policy. The resulting analysis is significant for at least two reasons. First, by elucidating the importance of discovery's earliest stages, it expands previous accounts by philosophers such as William Wimsatt and Lindley Darden. Second, it identifies the discovery process as yet another philosophical topic on which the detailed studies of the "new experimentalists" can shed new light. (shrink)

We describe an error type that we call the naturalizing error: an appeal to nature as a self-justified description dictating or limiting our choices in moral, economic, political, and other social contexts. Normative cultural perspectives may be subtly and subconsciously inscribed into purportedly objective descriptions of nature, often with the apparent warrant and authority of science, yet not be fully warranted by a systematic or complete consideration of the evidence. Cognitive processes may contribute further to a failure to notice the (...) lapses in scientific reasoning and justificatory warrant. By articulating this error type at a general level, we hope to raise awareness of this pervasive error type and to facilitate critiques of claims that appeal to what is “natural” as inevitable or unchangeable. (shrink)

What is the correct concept behind measures of inflation? Does money cause business activity or is it the other way around? Shall we stimulate growth by raising aggregate demand or rather by lowering taxes and thereby providing incentives to produce? Policy-relevant questions such as these are of immediate and obvious importance to the welfare of societies. The standard approach in dealing with them is to build a model, based on economic theory, answer the question for the model world and then (...) apply the results to economic phenomena outside. Data come in, if at all, only in testing a limited number of the model's consequences. Despite some critical voices, economic methodology too has by and large subscribed to a "theory first" approach to applied economics. Error in Economics systematically develops an alternative to the theory-based orthodoxy. It places the methodical study of evidence at the centre of the scientific enterprise and thus provides a foundation for a methodology of evidence-based economics. But the book does not stop at the truism that claims should be based on the best available evidence. Rather, detailed studies in the areas of measurement, causal inference and policy analysis show what it means for a claim to be evidence-based in the context of a concrete case. The examples discussed concern topics as diverse as consumer price indices, radio spectrum auctions, the transmission mechanism, natural experiments on minimum wages and the evaluation of counterfactuals for policy. Error in Economics is essential reading for economic methodologists, philosophers of science and anyone interested in how claims about socio-economic matters are validated. (shrink)

Current scientific research almost always requires collaboration among several (if not several hundred) specialized researchers. When scientists co-author a journal article, who deserves credit for discoveries or blame for errors? How should scientific institutions promote fruitful collaborations among scientists? In this book, leading philosophers of science address these critical questions.

This paper surveys contemporary accounts of error theory and fictionalism. It introduces these categories to those new to metaethics by beginning with moral nihilism, the view that nothing really is right or wrong. One main motivation is that the scientific worldview seems to have no place for rightness or wrongness. Within contemporary metaethics there is a family of theories that makes similar claims. These are the theories that are usually classified as forms of error theory or fictionalism though there are (...) different ways of accepting some form of the view that nothing is really write or wrong. A range of different ways of going in the light of such a realization is also proposed. The resulting taxonomy of positions is quite complicated and sometimes surprising. One surprise will be that some positions plausibly classified as error theories or forms of fictionalism do not quite seem to be forms of nihilism. (shrink)

This paper analyzes Deborah Mayo's error-statistical (ES) account of scientific evidence in order to clarify the kinds of "material postulates" it requires and to explain how those assumptions function. A secondary aim is to explain and illustrate the importance of the security of an inference. After finding that, on the most straightforward reading of the ES account, it does not succeed in its stated aims, two remedies are considered: either relativize evidence claims or introduce stronger assumptions. The choice between these (...) approaches turns on the value attached to two aims of inquiry that are in tension: drawing strong, informative conclusions and reasoning securely. (shrink)

One way in which philosophy of science can perform a valuable normative function for science is by showing characteristic errors made in scientific research programs and proposing ways in which such errors can be avoided or corrected. This paper examines two errors that have commonly plagued research in biology and psychology: 1) functional localization errors that arise when parts of a complex system are assigned functions which these parts are not themselves able to perform, and 2) vacuous functional explanations in (...) which one provides an analysis that does account for the inputs and outputs of a system but does not employ the same set of functions to produce this output as does the natural system. These two kinds of error usually arise when researchers limit their investigation to one type of evidence. Historically, correction of these errors has awaited researchers who have employed the opposite type of evidence. This paper explores the tendency to commit these errors by examining examples from historical and contemporary science and proposes a dialectical process through which researchers can avoid or correct such errors in the future. (shrink)

This chapter focuses on what’s novel in the perspective that the prediction error minimization (PEM) framework affords on the cognitive-scientific project of explaining intelligence by appeal to internal representations. It shows how truth-conditional and resemblance-based approaches to representation in generative models may be integrated. The PEM framework in cognitive science is an approach to cognition and perception centered on a simple idea: organisms represent the world by constantly predicting their own internal states. PEM theories often stress the hierarchical structure of (...) the generative models they posit. The novel explanatory power of the PEM account derives largely from the way in which pairs of generative and recognition models interact. “Predictive coding” refers to an encoding strategy in which predicted portions of an input signal are subtracted from the actual signal received, so that only the difference between the two is passed as output to the next stage of information processing. (shrink)

Kevin Elliott and others separate two common arguments for the legitimacy of societal values in scientific reasoning as the gap and the error arguments. This article poses two questions: How are these two arguments related, and what can we learn from their interrelation? I contend that we can better understand the error argument as nested within the gap because the error is a limited case of the gap with narrower features. Furthermore, this nestedness provides philosophers with conceptual tools for analyzing (...) more robustly how values pervade science. (shrink)

The power of literature to resist "totalization," to divide and oppose whole meaning, to separate Being from the word, or to name Being as itself divided—this is de Man's oldest and best-defended idea. Behind its deconstructionist and semiological variations in the recent work is a long genealogy of such insistence.6 This "genealogy" contains instructive continuities and aberrations. The continuities tend to show de Man to an extraordinary degree the captive of his beginnings. The aberrations pose a threat to the very (...) criterion of rigor which he makes the touchstone of his position. I will restrict myself here to an account of what is coherent and what is incoherent in de Man's treatment of the category of error.[…]Error is not mistake. The concept of the mistake is usable, perhaps, within the restricted teleology of pragmatic acts or within the quasi-rigorous language of scientific description. Mistakes are without true value: trivial, in principle corrigible according to a norm already known. But the skew of error implies a truth. Furthermore, the concept of error supplies to the categories of blindness and insight as much coherence as they are able to achieve. As we shall see, it brings together the constituents of the essential ambivalence of all literary and at least some philosophical language .6. In 1956, for instance, in a review of Nathalie Sarraute's L'Ère du Soupçon, de Man offered his reading of "the central moment of Ulysses, the carefully prepared encounter between Bloom and Stephan Dedalus": it "indicates, surely, the total impossiblity of any contact, of any human communication, even in the most disinterested love" .Stanley Corngold, professor of German and comparative literature at Princeton University, is the author of books and articles on Kafka, including The Commentator's Despair and an annotated translation of The Metamorphosis. A volume of his essays on the question of the self in Hölderlin, Dilthey, Nietzsche, Freud, Kafka, Mann, and Heidegger is forthcoming. (shrink)

This study is concerned with processes for discovering new theories in science. It considers a computational approach to scientific discovery, as applied to the discovery of theories in cognitive science. The approach combines two ideas. First, a process-based scientific theory can be represented as a computer program. Second, an evolutionary computational method, genetic programming, allows computer programs to be improved through a process of computational trialand-error. Putting these two ideas together leads to a system that can automatically generate and improve (...) scientific theories. The application of this method to the discovery of theories in cognitive science is examined. Theories are built up from primitive operators. These are contained in a theory language that defines the space of possible theories. An example of a theory generated by this method is described. These results support the idea that scientific discovery can be achieved through a heuristic search process, even for theories involving a sequence of steps. However, this computational approach to scientific discovery does not eliminate the need for human input. Human judgment is needed to make reasonable prior assumptions about the characteristics of operators used in the theory generation process, and to interpret and provide context for the computationally generated theories. (shrink)

This is a comment on Giora Hon's paper on scientific error.

The scientific reasoning strategies used to discover a new concept in a scientific domain were investigated in two studies. An innovative task in which subjects discover new concepts in molecular biology was used. This task was based upon one set of experiments that Jacob and Monod used to discover how genes are controlled, and for which they were awarded the Nobel prize. In the two studies reported in this article, subjects were taught some basic facts and experimental techniques in molecular (...) biology, using a simulated molecular genetics laboratory on a computer. Following their initial training, they were then asked to discover how genes are controlled by other genes. In Study 1, subjects found no evidence that was consistent with their initial hypothesis. Subjects then set one of two goals for conducting experiments and evaluating data. One goal was to search for evidence consistent with the current hypothesis (and they did not attend to the features of discrepant findings); none of the subjects who only had this goal succeeded at discovering how the genes were controlled. Other subjects in Study 1 used a different goal: Upon noticing evidence inconsistent with their current hypothesis, these subjects set a new goal of attempting to explain the cause of the discrepant findings. Using this goal, a subset of these subjects discovered the correct solution to the problem. Study 2 was conducted to test the hypothesis that subjects' goals of finding evidence consistent with their current hypothesis blocks consideration of alternate hypotheses and generation of new goals, it was predicted that if subjects could achieve their initial goal of discovering evidence consistent with their current hypothesis, they would then attend to particular features of discrepant evidence and solve the problem. To test this prediction, an additional mechanism of genetic control that was consistent with subjects' initial goal was added to the genes. Here, subjects had to discover two mechanisms of control: one mechanism consistent with their current hypothesis, and one inconsistent with their hypothesis. Twice as many subjects reached the correct solution in Study 2 than in Study 1. The findings of the two studies indicate that goals provide a powerful constraint on the cognitive processes underlying scientific reasoning and that the types of goals that are represented determine many of the reasoning errors that subjects make. (shrink)

We examined all 208 closed cases involving official findings of research misconduct published by the US Office of Research Integrity from 1992 to 2011 to determine how often scientists mention in a retraction or correction notice that there was an ethical problem with an associated article. 75 of these cases cited at least one published article affected by misconduct for a total of 174 articles. For 127 of these 174, we found both the article and a retraction or correction statement. (...) Since eight of the 127 published statements consisted of simply the word ‘retracted,’ our analysis focused on the remaining 119 for which a more detailed retraction or correction was published. Of these 119 statements, only 41.2% mentioned ethics at all (and only 32.8% named a specific ethical problem such as fabrication, falsification or plagiarism), whereas the other 58.8% described the reason for retraction or correction as error, loss of data or replication failure when misconduct was actually at issue. Among the published statements in response to an official finding of misconduct (within the time frame studied), the proportion that mentioned ethics was significantly higher in recent years than in earlier years, as was the proportion that named a specific problem. To promote research integrity, scientific journals should consider adopting policies concerning retractions and corrections similar to the guidelines developed by the Committee on Publication Ethics. Funding agencies and institutions should take steps to ensure that articles affected by misconduct are retracted or corrected. (shrink)

Various errors can affect scientific code and detecting them is a central concern within computational science. Could formal verification methods, which are now available tools, be widely adopted to guarantee the general reliability of scientific code? After discussing their benefits and drawbacks, we claim that, absent significant changes as regards features like their user-friendliness and versatility, these methods are unlikely to be adopted throughout computational science, beyond certain specific contexts for which they are well-suited. This issue exemplifies the epistemological heterogeneity (...) of computational science: profoundly different practices can be appropriate to meet the reliability challenge that rises for scientific code. (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.

Error is a neglected epistemological category in the history of science. This neglect has been driven by the commonsense idea that its elimination is a general good, which often renders it invisible or at least not worth noticing. At the end of the sixteenth century across Europe, medicine increasingly focused on “popular errors,” a genre where learned doctors addressed potential patients to disperse false belief about treatments. By the mid-seventeenth century, investigations into popular error informed the working methodology of natural (...) philosophers, rather than just physicians. In 1646, Thomas Browne published Pseudodoxia Epidemica, a large volume on popular error. Despite Browne’s formal training as a physician, this work examined only a few medical errors and instead aspired to be an encyclopedia of error. Pseudodoxia Epidemica was highly popular, running to six editions, and was known by the Fellows of the Royal Society. Influenced by Browne, alongside Bacon’s theory of the idols, natural philosophic practice in the late sixteenth and seventeenth century developed a focus on error that revised traditional attention to the discovery of knowledge. Fellows such as Robert Boyle and Robert Hooke proposed new ways to secure truth under the far-reaching influence of Bacon’s refutations of “natural human reason” distorted by false idols, of syllogistic logic, and of “theories,” his label for traditional philosophical systems that bias thought toward falsity. In three parts, this article traces the progression in early modern scientific approaches to handling error, and especially medical error – from physicians’ efforts to identify and eradicate it through collaborative effort, to the striking tension in Browne’s work between seeking to eliminate error while also showing a marked tolerance for it, to the Royal Society’s Baconian objective of instrumentalizing error to find truth. Error emerges as its own epistemic category that serves as a driving force toward knowledge production. (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)

Interest in the computational aspects of modeling has been steadily growing in philosophy of science. This paper aims to advance the discussion by articulating the way in which modeling and computational errors are related and by explaining the significance of error management strategies for the rational reconstruction of scientific practice. To this end, we first characterize the role and nature of modeling error in relation to a recipe for model construction known as Euler’s recipe. We then describe a general model (...) that allows us to assess the quality of numerical solutions in terms of measures of computational errors that are completely interpretable in terms of modeling error. Finally, we emphasize that this type of error analysis involves forms of perturbation analysis that go beyond the basic model-theoretical and statistical/probabilistic tools typically used to characterize the scientific method; this demands that we revise and complement our reconstructive toolbox in a way that can affect our normative image of science. (shrink)

Recent work by Frigg et. al. and Mayo-Wilson have called attention to a particular sort of error associated with attempts to model certain complex systems: structural modeling error. The assessment of the degree of SME in a model presupposes agreement between modelers about the best way to individuate natural systems, an agreement which can be more problematic than it appears. This problem, which we dub “the system individuation problem” arises in many of the same contexts as SME, and the two (...) often compound one another. This paper explores the common roots of the two problems in concerns about the precision of predictions generated by scientific models, and discusses how both concerns bear on the study of complex natural systems, particularly the global climate. (shrink)

In the last few decades the role played by models and modeling activities has become a central topic in the scientific enterprise. In particular, it has been highlighted both that the development of models constitutes a crucial step for understanding the world and that the developed models operate as mediators between theories and the world. Such perspective is exploited here to cope with the issue as to whether error-based and uncertainty-based modeling of measurement are incompatible, and thus alternative with one (...) another, as sometimes claimed nowadays. The crucial problem is whether assuming this standpoint implies definitely renouncing to maintain a role for truth and the related concepts, particularly accuracy, in measurement. It is argued here that the well known objections against true values in measurement, which would lead to refuse the concept of accuracy as non-operational, or to maintain it as only qualitative, derive from a not clear distinction between three distinct processes: the metrological characterization of measuring systems, their calibration, and finally measurement. Under the hypotheses that (1) the concept of true value is related to the model of a measurement process, (2) the concept of uncertainty is related to the connection between such model and the world, and (3) accuracy is a property of measuring systems (and not of measurement results) and uncertainty is a property of measurement results (and not of measuring systems), not only the compatibility but actually the conjoint need of error-based and uncertainty-based modeling emerges. (shrink)

Etymologically, pseudoscience means false science. The term is often explained as referring to claims or teachings that are presented as scientific but are in fact not scientific. Any understanding of the notion of pseudoscience must therefore build on both an understanding of what science is and a clarification of what forms of errors or misrepresentations make a statement pseudoscientific instead of scientific.

Next SectionBackground Scientific papers are retracted for many reasons including fraud (data fabrication or falsification) or error (plagiarism, scientific mistake, ethical problems). Growing attention to fraud in the lay press suggests that the incidence of fraud is increasing. Methods The reasons for retracting 742 English language research papers retracted from the PubMed database between 2000 and 2010 were evaluated. Reasons for retraction were initially dichotomised as fraud or error and then analysed to determine specific reasons for retraction. Results Error was (...) more common than fraud (73.5% of papers were retracted for error (or an undisclosed reason) vs 26.6% retracted for fraud). Eight reasons for retraction were identified; the most common reason was scientific mistake in 234 papers (31.5%), but 134 papers (18.1%) were retracted for ambiguous reasons. Fabrication (including data plagiarism) was more common than text plagiarism. Total papers retracted per year have increased sharply over the decade (r=0.96; p<0.001), as have retractions specifically for fraud (r=0.89; p<0.001). Journals now reach farther back in time to retract, both for fraud (r=0.87; p<0.001) and for scientific mistakes (r=0.95; p<0.001). Journals often fail to alert the naïve reader; 31.8% of retracted papers were not noted as retracted in any way. Conclusions Levels of misconduct appear to be higher than in the past. This may reflect either a real increase in the incidence of fraud or a greater effort on the part of journals to police the literature. However, research bias is rarely cited as a reason for retraction. (shrink)

Grzegorz W. Kolodko, one of the world's leading authorities on economics and development policy and a key architect of Poland's successful economic reforms, applies his far-reaching knowledge to the past and future of the world economy, introducing a framework for understanding our global situation that transcends any single discipline or paradigm. Deploying a novel mix of scientific evaluation and personal observation, Kolodko begins with a brief discussion of misinformation and its perpetuation in economics and politics. He criticizes the simplification of (...) complex economic and social issues and investigates the link between developments in the global economy and cultural change, scientific discoveries, and political fluctuations. Underscoring the necessity of conceptual and theoretical innovation in understanding our global economic situation, Kolodko offers a provocative study of globalization and the possibility of coming out ahead in an era of worldwide interdependence. Deeply critical of neoliberalism, which sought to transfer economic control exclusively to the private sector, Kolodko explores the virtues of social-economic development and the new rules of the economic game. He concludes with a look at our near and distant future, questioning whether we have a say in its making. (shrink)

An important theme to have emerged from the new experimentalist movement is that much of actual scientific practice deals not with appraising full-blown theories but with the manifold local tasks required to arrive at data, distinguish fact from artifact, and estimate backgrounds. Still, no program for working out a philosophy of experiment based on this recognition has been demarcated. I suggest why the new experimentalism has come up short, and propose a remedy appealing to the practice of standard error statistics. (...) I illustrate a portion of my proposal using Galison's experimental narrative on neutral currents. (shrink)

Feyerabend's views are construed as formulating the problem of determining the role of rhetoric in scientific rationality and posing the solution-theory that scientific rationality is essentially rhetorical. He is taken to give three arguments against reason, of which the one from the insufficiency of reason and the one from incommensurability are shown to presuppose his historical argument; his historical argument is based on his account of Galileo, which hinges essentially on Feyerabend's analysis of the tower argument. This analysis is insightful (...) in certain important ways but misconceived in others. Feyerabend's main error is to see a conflict between reason and rhetoric, where none exists; it is argued that rhetorical devices have their own standards of propriety and impropriety, different from those of rational arguments, and that at the same time sound rhetorical analysis presupposes sound logical analysis. (shrink)

This paper provides examples drawn from the author’s experience that support the conclusion that errors and deceptions in archival science are often not easily or quickly corrected. The difficulty in correcting errors and deceptions needs wider recognition if it is to be overcome. In addition, the paper discusses how subtle abuses introduce errors into the archival literature.

This short paper serves as an introduction to a debate between representatives of two fundamentally different points of view regarding the nature of scientific inference. Colin Howson and Peter Urbach represent a Bayesian point of view and Deborah Mayo represents a version of classical statistics called error statistics. The paper begins by reviewing earlier versions of the same two points of view due to Rudolf Carnap and Hans Reichenbach, respectively. After a few remarks about philosophical approaches to understanding scientific reasoning (...) between 1960 and 1980, I turn to substantive differences between the two approaches. (shrink)

Error statistics is an important methodological view in philosophy of statistics and philosophy of science that can be applied to scientific experiments such as clinical trials. In this paper, I raise two potential issues for ES when it comes to guiding, and explaining early stopping of randomized controlled trials : ES provides limited guidance in cases of early unfavorable trends due to the possibility of trend reversal; ES is silent on how to prospectively control error rates in experiments requiring multiple (...) interim analyses. The method of conditional power, together with a rationing principle for RCTs, can assist ES in addressing such issues. (shrink)