Search results for 'statistical evidence and inference' (try it on Scholar)

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  1. David L. Dowe (2008). Minimum Message Length and Statistically Consistent Invariant (Objective?) Bayesian Probabilistic Inference—From (Medical) “Evidence”. Social Epistemology 22 (4):433 – 460.score: 336.0
    Evidence” in the form of data collected and analysis thereof is fundamental to medicine, health and science. In this paper, we discuss the “evidence-based” aspect of evidence-based medicine in terms of statistical inference, acknowledging that this latter field of statistical inference often also goes by various near-synonymous names—such as inductive inference (amongst philosophers), econometrics (amongst economists), machine learning (amongst computer scientists) and, in more recent times, data mining (in some circles). Three central (...)
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  2. Stephanie Denison & Fei Xu (2010). Integrating Physical Constraints in Statistical Inference by 11-Month-Old Infants. Cognitive Science 34 (5):885-908.score: 333.0
    Much research on cognitive development focuses either on early-emerging domain-specific knowledge or domain-general learning mechanisms. However, little research examines how these sources of knowledge interact. Previous research suggests that young infants can make inferences from samples to populations (Xu & Garcia, 2008) and 11- to 12.5-month-old infants can integrate psychological and physical knowledge in probabilistic reasoning (Teglas, Girotto, Gonzalez, & Bonatti, 2007; Xu & Denison, 2009). Here, we ask whether infants can integrate a physical constraint of immobility into a (...) inference mechanism. Results from three experiments suggest that, first, infants were able to use domain-specific knowledge to override statistical information, reasoning that sometimes a physical constraint is more informative than probabilistic information. Second, we provide the first evidence that infants are capable of applying domain-specific knowledge in probabilistic reasoning by using a physical constraint to exclude one set of objects while computing probabilities over the remaining sets. (shrink)
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  3. Philip Dawid, William Twining & Mimi Vasilaki (eds.) (2011). Evidence, Inference and Enquiry. OUP/British Academy.score: 276.3
    Evidence - its nature and interpretation - is the key to many topical debates and concerns such as global warming, evolution, the search for weapons of mass destruction, DNA profiling, evidence-based medicine. In 2004 University College London launched a cross-disciplinary research programme "Evidence, Inference and Enquiry" to explore the question: "Can there be an integrated multidisciplinary science of evidence?" While this question was hotly contested and no clear final consensus emerged, much was learned on the (...)
     
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  4. Kent W. Staley, Strategies for Securing Evidence Through Model Criticism: An Error-Statistical Perspective.score: 267.0
    : I propose an epistemological extension of the error-statistical (ES) account of inference advocated by Deborah Mayo. To supplement the unrelativized account of evidence provided by ES, I propose a relativized notion, which I designate security, meant to conceptualize practices aimed at the justification of inferences from evidence. I then show how the notion of security can be put to use by showing how two very different theoretical approaches to model criticism in statistics can both be (...)
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  5. Kent Staley, Can Error-Statistical Inference Function Securely?score: 261.0
    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. (...)
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  6. Roger Stanev (2009). Epidemiologic Causation: Jerome Cornfield’s Argument for a Causal Connection Between Smoking and Lung Cancer. Humana.Mente 9:59-66.score: 243.0
    A central issue confronting both philosophers and practitioners in formulating an analysis of causation is the question of what constitutes evidence for a causal association. From the 1950s onward, the biostatistician Jerome Cornfield put himself at the center of a controversial debate over whether cigarette smoking was a causative factor in the incidence of lung cancer. Despite criticisms from distinguished statisticians such as Fisher, Berkson and Neyman, Cornfield argued that a review of the scientific evidence supported the conclusion (...)
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  7. David Enoch, Levi Spectre & Talia Fisher (2012). Statistical Evidence, Sensitivity, and the Legal Value of Knowledge. Philosophy and Public Affairs 40 (3):197-224.score: 224.0
    The law views with suspicion statistical evidence, even evidence that is probabilistically on a par with direct, individual evidence that the law is in no way suspicious of. But it has proved remarkably hard to either justify this suspicion, or to debunk it. In this paper, we connect the discussion of statistical evidence to broader epistemological discussions of similar phenomena. We highlight Sensitivity – the requirement that a belief be counterfactually sensitive to the truth (...)
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  8. Margaret MacDougall (forthcoming). Assessing the Integrity of Clinical Data: When is Statistical Evidence Too Good to Be True? Topoi:1-15.score: 220.0
    Evidence, as viewed through the lens of statistical significance, is not always as it appears! In the investigation of clinical research findings arising from statistical analyses, a fundamental initial step for the emerging fraud detective is to retrieve the source data for cross-examination with the study data. Recognizing that source data are not always forthcoming and that, realistically speaking, the investigator may be uninitiated in fraud detection and investigation, this paper will highlight some key methodological procedures for (...)
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  9. Roger Stanev (2011). Statistical Decisions and the Interim Analyses of Clinical Trials. Theoretical Medicine and Bioethics 32 (1):61-74.score: 216.0
    This paper analyzes statistical decisions during the interim analyses of clinical trials. After some general remarks about the ethical and scientific demands of clinical trials, I introduce the notion of a hard-case clinical trial, explain the basic idea behind it, and provide a real example involving the interim analyses of zidovudine in asymptomatic HIV-infected patients. The example leads me to propose a decision analytic framework for handling ethical conflicts that might arise during the monitoring of hard-case clinical trials. I (...)
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  10. Deborah G. Mayo (2000). Experimental Practice and an Error Statistical Account of Evidence. Philosophy of Science 67 (3):207.score: 216.0
    In seeking general accounts of evidence, confirmation, or inference, philosophers have looked to logical relationships between evidence and hypotheses. Such logics of evidential relationship, whether hypothetico-deductive, Bayesian, or instantiationist fail to capture or be relevant to scientific practice. They require information that scientists do not generally have (e.g., an exhaustive set of hypotheses), while lacking slots within which to include considerations to which scientists regularly appeal (e.g., error probabilities). Building on my co-symposiasts contributions, I suggest some directions (...)
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  11. Shimon Edelman, Unsupervised Statistical Learning in Vision: Computational Principles, Biological Evidence.score: 216.0
    Unsupervised statistical learning is the standard setting for the development of the only advanced visual system that is both highly sophisticated and versatile, and extensively studied: that of monkeys and humans. In this extended abstract, we invoke philosophical observations, computational arguments, behavioral data and neurobiological findings to explain why computer vision researchers should care about (1) unsupervised learning, (2) statistical inference, and (3) the visual brain. We then outline a neuromorphic approach to structural primitive learning motivated by (...)
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  12. Lili Ma & Fei Xu (2011). Young Children's Use of Statistical Sampling Evidence to Infer the Subjectivity of Preferences. Cognition 120 (3):403-411.score: 215.0
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  13. David Godden (2014). Modeling Corroborative Evidence: Inference to the Best Explanation as Counter–Rebuttal. Argumentation 28 (2):187-220.score: 212.0
    Corroborative evidence has a dual function in argument. Primarily, it functions to provide direct evidence supporting the main conclusion. But it also has a secondary, bolstering function which increases the probative value of some other piece of evidence in the argument. This paper argues that the bolstering effect of corroborative evidence is legitimate, and can be explained as counter–rebuttal achieved through inference to the best explanation. A model (argument diagram) of corroborative evidence, representing its (...)
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  14. Roger Stanev (2012). Stopping Rules and Data Monitoring in Clinical Trials. In H. W. de Regt, S. Hartmann & S. Okasha (eds.), EPSA Philosophy of Science: Amsterdam 2009, The European Philosophy of Science Association Proceedings Vol. 1, 375-386. Springer. 375--386.score: 210.0
    Stopping rules — rules dictating when to stop accumulating data and start analyzing it for the purposes of inferring from the experiment — divide Bayesians, Likelihoodists and classical statistical approaches to inference. Although the relationship between Bayesian philosophy of science and stopping rules can be complex (cf. Steel 2003), in general, Bayesians regard stopping rules as irrelevant to what inference should be drawn from the data. This position clashes with classical statistical accounts. For orthodox statistics, stopping (...)
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  15. Eyal Shahar (1997). A Popperian Perspective of the Term 'Evidence‐Based Medicine'. Journal of Evaluation in Clinical Practice 3 (2):109-116.score: 207.0
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  16. Kent Staley (2012). Strategies for Securing Evidence Through Model Criticism. European Journal for Philosophy of Science 2 (1):21-43.score: 204.0
    Some accounts of evidence regard it as an objective relationship holding between data and hypotheses, perhaps mediated by a testing procedure. Mayo’s error-statistical theory of evidence is an example of such an approach. Such a view leaves open the question of when an epistemic agent is justified in drawing an inference from such data to a hypothesis. Using Mayo’s account as an illustration, I propose a framework for addressing the justification question via a relativized notion, which (...)
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  17. Barbara Osimani (2013). Until RCT-Proven? On the Asymmetry of Evidence Requirements for Risk Assessment. Journal of Evaluation in Clinical Practice 19 (3):454-462.score: 198.0
    The problem of collecting, analyzing and evaluating evidence on adverse drug reactions (ADRs) is an example of the more general class of epistemological problems related to scientific inference and prediction, as well as a central problem of the health-care practice. Philosophical discussions have critically analysed the methodological pitfalls and epistemological implications of evidence assessment in medicine, however they have mainly focused on evidence of treatment efficacy. Most of this work is devoted to statistical methods of (...)
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  18. Lyle C. Gurrin, Peter D. Sly & Paul R. Burton (2002). Using Imprecise Probabilities to Address the Questions of Inference and Decision in Randomized Clinical Trials. Journal of Evaluation in Clinical Practice 8 (2):255-268.score: 198.0
    Randomized controlled clinical trials play an important role in the development of new medical therapies. There is, however, an ethical issue surrounding the use of randomized treatment allocation when the patient is suffering from a life threatening condition and requires immediate treatment. Such patients can only benefit from the treatment they actually receive and not from the alternative therapy, even if it ultimately proves to be superior. We discuss a novel new way to analyse data from such clinical trials based (...)
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  19. James V. Allen (2001). Inference From Signs: Ancient Debates About the Nature of Evidence. Oxford University Press.score: 192.0
    Original and penetrating, this book investigates of the notion of inference from signs, which played a central role in ancient philosophical and scientific method. It examines an important chapter in ancient epistemology: the debates about the nature of evidence and of the inferences based on it--or signs and sign-inferences as they were called in antiquity. As the first comprehensive treatment of this topic, it fills an important gap in the histories of science and philosophy.
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  20. M. Coulson, M. Healey, F. Fidler & G. Cumming (2009). Confidence Intervals Permit, but Do Not Guarantee, Better Inference Than Statistical Significance Testing. Frontiers in Psychology 1:26-26.score: 192.0
    A statistically significant result, and a non-significant result may differ little, although significance status may tempt an interpretation of difference. Two studies are reported that compared interpretation of such results presented using null hypothesis significance testing (NHST), or confidence intervals (CIs). Authors of articles published in psychology, behavioural neuroscience, and medical journals were asked, via email, to interpret two fictitious studies that found similar results, one statistically significant, and the other non-significant. Responses from 330 authors varied greatly, but interpretation was (...)
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  21. Roger Stanev (2012). Modelling and Simulating Early Stopping of RCTs: A Case Study of Early Stop Due to Harm. Journal of Experimental and Theoretical Artificial Intelligence 24 (4):513-526.score: 174.0
    Despite efforts from regulatory agencies (e.g. NIH, FDA), recent systematic reviews of randomised controlled trials (RCTs) show that top medical journals continue to publish trials without requiring authors to report details for readers to evaluate early stopping decisions carefully. This article presents a systematic way of modelling and simulating interim monitoring decisions of RCTs. By taking an approach that is both general and rigorous, the proposed framework models and evaluates early stopping decisions of RCTs based on a clear and consistent (...)
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  22. Daniel Barker (forthcoming). Seeing the Wood for the Trees: Philosophical Aspects of Classical, Bayesian and Likelihood Approaches in Statistical Inference and Some Implications for Phylogenetic Analysis. Biology and Philosophy:1-21.score: 170.7
    The three main approaches in statistical inference—classical statistics, Bayesian and likelihood—are in current use in phylogeny research. The three approaches are discussed and compared, with particular emphasis on theoretical properties illustrated by simple thought-experiments. The methods are problematic on axiomatic grounds (classical statistics), extra-mathematical grounds relating to the use of a prior (Bayesian inference) or practical grounds (likelihood). This essay aims to increase understanding of these limits among those with an interest in phylogeny.
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  23. Carl Cranor & Kurt Nutting (1990). Scientific and Legal Standards of Statistical Evidence in Toxic Tort and Discrimination Suits. Law and Philosophy 9 (2):115 - 156.score: 168.0
    Many legal disputes turn on scientific, especially statistical, evidence. Traditionally scientists have accepted only that statistical evidence which satisfies a 95 percent (or 99 percent) rule — that is, only evidence which has less than five percent (or one percent) probability of resulting from chance.The rationale for this rule is the reluctance of scientists to accept anything less than the best-supported new knowledge. The rule reflects the internal needs of scientific practice. However, when uncritically adopted (...)
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  24. Alastair McKinnon (1984). Kierkegaard's Interpretation of His 'Authorship': Some Statistical Evidence. Inquiry 27 (1-4):225 – 233.score: 168.0
    In The Point of View for my Work as an Author, Kierkegaard declares that the works discussed therein move from the aesthetic to the religious, that the Postscript represents the turning point in this movement, etc. In this brief and preliminary study we use a ?change?point? version of the chi?square test on the frequencies of selected sets of ?aesthetic? and ?religious? words to determine the degree of statistical evidence for these and other related claims. Briefly, these tests show (...)
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  25. Philip Brickman & Scott M. Pierce (1972). Estimates of Conditional Probabilities of Confirming Versus Disconfirming Events as a Function of Inference Situation and Prior Evidence. Journal of Experimental Psychology 95 (1):235.score: 168.0
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  26. William L. Twining & Iain Hampsher-Monk (eds.) (2003). Evidence and Inference in History and Law: Interdisciplinary Dialogues. Northwestern University Press.score: 168.0
  27. Kent Staley (2008). Error-Statistical Elimination of Alternative Hypotheses. Synthese 163 (3):397 - 408.score: 166.0
    I consider the error-statistical account as both a theory of evidence and as a theory of inference. I seek to show how inferences regarding the truth of hypotheses can be upheld by avoiding a certain kind of alternative hypothesis problem. In addition to the testing of assumptions behind the experimental model, I discuss the role of judgments of implausibility. A benefit of my analysis is that it reveals a continuity in the application of error-statistical assessment to (...)
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  28. Roger Stanev (2012). The Epistemology and Ethics of Early Stopping Decisions in Randomized Controlled Trials. Dissertation, University of British Columbiascore: 165.0
    Philosophers subscribing to particular principles of statistical inference and evidence need to be aware of the limitations and practical consequences of the statistical approach they endorse. The framework proposed (for statistical inference in the field of medicine) allows disparate statistical approaches to emerge in their appropriate context. My dissertation proposes a decision theoretic model, together with methodological guidelines, that provide important considerations for deciding on clinical trial conduct. These considerations do not amount to (...)
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  29. Julian Reiss (2009). Causation in the Social Sciences: Evidence, Inference, and Purpose. Philosophy of the Social Sciences 39 (1):20-40.score: 164.0
    All univocal analyses of causation face counterexamples. An attractive response to this situation is to become a pluralist about causal relationships. "Causal pluralism" is itself, however, a pluralistic notion. In this article, I argue in favor of pluralism about concepts of cause in the social sciences. The article will show that evidence for, inference from, and the purpose of causal claims are very closely linked. Key Words: causation • pluralism • evidence • methodology.
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  30. Chen Yu, Yiwen Zhong & Damian Fricker (2012). Selective Attention in Cross-Situational Statistical Learning: Evidence From Eye Tracking. Frontiers in Psychology 3.score: 160.0
    A growing set of data show that adults are quite good at accumulating statistical evidence across individually ambiguous learning contexts with multiple novel words and multiple novel objects (Fitneva & Christiansen, 2011; Kachergis, Yu, & Shiffrin, 2012; Yu & Smith, 2007; Yurovsky, Fricker, Yu, & Smith, under resubmission); experimental studies also indicate that infants and young children do this kind of learning as well (Smith & Yu, 2008; Vouloumanos & Werker, 2009). The present study provides evidence for (...)
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  31. Paul D. Thorn (2014). Defeasible Conditionalization. Journal of Philosophical Logic 43:283-302.score: 153.0
    The applicability of Bayesian conditionalization in setting one’s posterior probability for a proposition, α, is limited to cases where the value of a corresponding prior probability, PPRI(α|∧E), is available, where ∧E represents one’s complete body of evidence. In order to extend probability updating to cases where the prior probabilities needed for Bayesian conditionalization are unavailable, I introduce an inference schema, defeasible conditionalization, which allows one to update one’s personal probability in a proposition by conditioning on a proposition that (...)
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  32. Sinan Dogramaci (2014). A Problem for Rationalist Responses to Skepticism. Philosophical Studies 168 (2):355-369.score: 153.0
    Rationalism, my target, says that in order to have perceptual knowledge, such as that your hand is making a fist, you must “antecedently” (or “independently”) know that skeptical scenarios don’t obtain, such as the skeptical scenario that you are in the Matrix. I motivate the specific form of Rationalism shared by, among others, White (Philos Stud 131:525–557, 2006) and Wright (Proc Aristot Soc Suppl Vol 78:167–212, 2004), which credits us with warrant to believe (or “accept”, in Wright’s terms) that our (...)
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  33. John W. Pratt (1977). Decisions as Statistical Evidence and Birnbaum's 'Confidence Concept'. Synthese 36 (1):59 - 69.score: 146.0
    To whatever extent the use of a behavioral, not an evidential, interpretation of decisions in the Lindley-Savage argument for Bayesian theory undermines its cogency as a criticism of typical standard practice, it also undermines the Neyman-Pearson theory as a support for typical standard practice. This leaves standard practice with far less theoretical support than Bayesian methods. It does nothing to resolve the anomalies and paradoxes of standard methods. (Similar statements apply to the common protestation that the models are not real (...)
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  34. Harmon R. Holcomb (1996). Just so Stories and Inference to the Best Explanation in Evolutionary Psychology. Minds and Machines 6 (4):525-540.score: 144.0
    Evolutionary psychology is a science in the making, working toward the goal of showing how psychological adaptation underlies much human behavior. The knee-jerk reaction that sociobiology is unscientific because it tells just-so stories has become a common charge against evolutionary psychology as well. My main positive thesis is that inference to the best explanation is a proper method for evolutionary analyses, and it supplies a new perspective on the issues raised in Schlinger's (1996) just-so story critique. My main negative (...)
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  35. Johannes Lenhard (2006). Models and Statistical Inference: The Controversy Between Fisher and Neyman–Pearson. British Journal for the Philosophy of Science 57 (1):69-91.score: 144.0
    The main thesis of the paper is that in the case of modern statistics, the differences between the various concepts of models were the key to its formative controversies. The mathematical theory of statistical inference was mainly developed by Ronald A. Fisher, Jerzy Neyman, and Egon S. Pearson. Fisher on the one side and Neyman–Pearson on the other were involved often in a polemic controversy. The common view is that Neyman and Pearson made Fisher's account more stringent mathematically. (...)
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  36. Aris Spanos (2007). Curve Fitting, the Reliability of Inductive Inference, and the Error-Statistical Approach. Philosophy of Science 74 (5):1046-1066.score: 144.0
    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 (...)
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  37. Amade M'charek (2008). Silent Witness, Articulate Collective: Dna Evidence and the Inference of Visible Traits. Bioethics 22 (9):519-528.score: 144.0
    DNA profiling is a well-established technology for use in the criminal justice system, both in courtrooms and elsewhere. The fact that DNA profiles are based on non-coding DNA and do not reveal details about the physical appearance of an individual has contributed to the acceptability of this type of evidence. Its success in criminal investigation, combined with major innovations in the field of genetics, have contributed to a change of role for this type of evidence. Nowadays DNA (...) is not merely about identification, where trace evidence is compared to a sample taken from a suspect. An ever-growing role is anticipated for DNA profiling as an investigative tool, a technique aimed at generating a suspect where there is none. One of these applications is the inference of visible traits. As this article will show, racial classifications are at the heart of this application. The Netherlands and its legal regulation of 'externally visible traits' will serve as an example. It will be shown that, to make this technology work, a large number of actors has to be enrolled and their articulations invited. This indicates that instead of a 'silent witness', a DNA profile should rather be seen as an 'articulate collective'. Based on two cases, I argue that the normativity of visible traits is context-dependent. Taking into account the practices in which technology is put to use alerts us to novel ethical questions raised by their application. (shrink)
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  38. Jan Sprenger, Surprise and Evidence in Statistical Model Checking.score: 144.0
    There is considerable confusion about the role of p-values in statistical model checking. To clarify that point, I introduce the distinction between measures of surprise and measures of evidence which come with different epistemological functions. I argue that p-values, often understood as measures of evidence against a null model, do not count as proper measures of evidence and are closer to measures of surprise. Finally, I sketch how the problem of old evidence may be tackled (...)
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  39. Katie Steele (2013). Persistent Experimenters, Stopping Rules, and Statistical Inference. Erkenntnis 78 (4):937-961.score: 144.0
    This paper considers a key point of contention between classical and Bayesian statistics that is brought to the fore when examining so-called ‘persistent experimenters’—the issue of stopping rules, or more accurately, outcome spaces, and their influence on statistical analysis. First, a working definition of classical and Bayesian statistical tests is given, which makes clear that (1) once an experimental outcome is recorded, other possible outcomes matter only for classical inference, and (2) full outcome spaces are nevertheless relevant (...)
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  40. Henry Kyburg (1974). The Logical Foundations of Statistical Inference. Reidel.score: 144.0
    At least one of these conceptions of probability underlies any theory of statistical inference (or, to use Neyman's phrase, 'inductive behavior'). ...
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  41. Miklós Rédei (1992). When Can Non-Commutative Statistical Inference Be Bayesian? International Studies in the Philosophy of Science 6 (2):129 – 132.score: 144.0
    Based on recalling two characteristic features of Bayesian statistical inference in commutative probability theory, a stability property of the inference is pointed out, and it is argued that that stability of the Bayesian statistical inference is an essential property which must be preserved under generalization of Bayesian inference to the non-commutative case. Mathematical no-go theorems are recalled then which show that, in general, the stability can not be preserved in non-commutative context. Two possible interpretations (...)
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  42. Fei Xu & Joshua B. Tenenbaum (2001). Rational Statistical Inference: A Critical Component for Word Learning. Behavioral and Brain Sciences 24 (6):1123-1124.score: 144.0
    In order to account for how children can generalize words beyond a very limited set of labeled examples, Bloom's proposal of word learning requires two extensions: a better understanding of the “general learning and memory abilities” involved, and a principled framework for integrating multiple conflicting constraints on word meaning. We propose a framework based on Bayesian statistical inference that meets both of those needs.
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  43. Rodney W. Benoist, Jean-Paul Marchand & Wolfgang Yourgrau (1977). Statistical Inference and Quantum Mechanical Measurement. Foundations of Physics 7 (11-12):827-833.score: 144.0
    We analyze the quantum mechanical measuring process from the standpoint of information theory. Statistical inference is used in order to define the most likely state of the measured system that is compatible with the readings of the measuring instrument and the a priori information about the correlations between the system and the instrument. This approach has the advantage that no reference to the time evolution of the combined system need be made. It must, however, be emphasized that the (...)
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  44. Miklós Rédei (1992). When Can Non‐Commutative Statistical Inference Be Bayesian? International Studies in the Philosophy of Science 6 (2):129-132.score: 144.0
    Abstract Based on recalling two characteristic features of Bayesian statistical inference in commutative probability theory, a stability property of the inference is pointed out, and it is argued that that stability of the Bayesian statistical inference is an essential property which must be preserved under generalization of Bayesian inference to the non?commutative case. Mathematical no?go theorems are recalled then which show that, in general, the stability can not be preserved in non?commutative context. Two possible (...)
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  45. Harmon R. Holcomb Iii (1996). Just so Stories and Inference to the Best Explanation in Evolutionary Psychology. Minds and Machines 6 (4):525-540.score: 144.0
    Evolutionary psychology is a science in the making, working toward the goal of showing how psychological adaptation underlies much human behavior. The knee-jerk reaction that sociobiology is unscientific because it tells “just-so stories” has become a common charge against evolutionary psychology as well. My main positive thesis is that inference to the best explanation is a proper method for evolutionary analyses, and it supplies a new perspective on the issues raised in Schlinger's (1996) just-so story critique. My main negative (...)
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  46. Barbara Osimani (2013). Hunting Side Effects and Explaining Them: Should We Reverse Evidence Hierarchies Upside Down? [REVIEW] Journal of Evaluation in Clinical Practice:1-18.score: 144.0
    The problem of collecting, analyzing and evaluating evidence on adverse drug reactions (ADRs) is an example of the more general class of epistemological problems related to scientific inference and prediction, as well as a central problem of the health-care practice. Philosophical discussions have critically analysed the methodological pitfalls and epistemological implications of evidence assessment in medicine, however they have mainly focused on evidence of treatment efficacy. Most of this work is devoted to statistical methods of (...)
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  47. David Schum (2003). Evidence and Inference About Past Events: An Overview of Six Case Studies. In William L. Twining & Iain Hampsher-Monk (eds.), Evidence and Inference in History and Law: Interdisciplinary Dialogues. Northwestern University Press. 9--62.score: 144.0
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  48. Zoltan Dienes (2008). Understanding Psychology as a Science: An Introduction to Scientific and Statistical Inference. Palgrave Macmillan.score: 144.0
    An accessible and illuminating exploration of the conceptual basisof scientific and statistical inference and the practical impact this has on conducting psychological research. The book encourages a critical discussion of the different approaches and looks at some of the most important thinkers and their influence.
     
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  49. C. Hennig (2009). A Constructivist View of the Statistical Quantification of Evidence. Constructivist Foundations 5 (1):39 - 54.score: 144.0
    Problem: Evidence is quantified by statistical methods such as p-values and Bayesian posterior probabilities in a routine way despite the fact that there is no consensus about the meanings and implications of these approaches. A high level of confusion about these methods can be observed among students, researchers and even professional statisticians. How can a constructivist view of mathematical models and reality help to resolve the confusion? Method: Considerations about the foundations of statistics and probability are revisited with (...)
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  50. D. Lerner (1959). Introduction: On Evidence and Inference. In Daniel Lerner (ed.), Evidence and Inference. Chicago, Free Press of Glencoe. 7--18.score: 144.0
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