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  1. Statistical Inference and the Replication Crisis.Lincoln J. Colling & Dénes Szűcs - 2021 - Review of Philosophy and Psychology 12 (1):121-147.
    The replication crisis has prompted many to call for statistical reform within the psychological sciences. Here we examine issues within Frequentist statistics that may have led to the replication crisis, and we examine the alternative—Bayesian statistics—that many have suggested as a replacement. The Frequentist approach and the Bayesian approach offer radically different perspectives on evidence and inference with the Frequentist approach prioritising error control and the Bayesian approach offering a formal method for quantifying the relative strength of evidence for hypotheses. (...)
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  2. Reliability: An Introduction.Stefano Bonzio, Jürgen Landes & Barbara Osimani - 2020 - Synthese:1-10.
  3. Erratum to: The Jeffreys–Lindley paradox and discovery criteria in high energy physics.Robert D. Cousins - 2020 - Synthese 197 (8):3643-3643.
  4. Scientific Self-Correction: The Bayesian Way.Felipe Romero & Jan Sprenger - 2020 - Synthese:1-21.
    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 (...)
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  5. How to Handle Risky Experiments Producing Uncertain Phenomenon Like Cold Fusion?Robert W. P. Luk - 2019 - Science and Philosophy 7 (2):3-14.
    Some experiments are risky in that they cannot repeatedly produce certain phenomenon at will for study because the scientific knowledge of the process generating the uncertain phenomenon is poorly understood or may directly contradict with existing scientific knowledge. These experiments may have great impact not just to the scientific community but to mankind in general. Banning them from study may incur societies a great opportunity cost but accepting them runs the risk that scientists are doing junk science. How to make (...)
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  6. Why is Bayesian Confirmation Theory Rarely Practiced?Robert W. P. Luk - 2019 - Science and Philosophy 7 (1):3-20.
    Bayesian confirmation theory is a leading theory to decide the confirmation/refutation of a hypothesis based on probability calculus. While it may be much discussed in philosophy of science, is it actually practiced in terms of hypothesis testing by scientists? Since the assignment of some of the probabilities in the theory is open to debate and the risk of making the wrong decision is unknown, many scientists do not use the theory in hypothesis testing. Instead, they use alternative statistical tests that (...)
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  7. Should Scientific Realists Embrace Theoretical Conservatism?Finnur Dellsén - 2018 - Studies in History and Philosophy of Science Part A:30-38.
    A prominent type of scientific realism holds that some important parts of our best current scientific theories are at least approximately true. According to such realists, radically distinct alternatives to these theories or theory-parts are unlikely to be approximately true. Thus one might be tempted to argue, as the prominent anti-realist Kyle Stanford recently did, that realists of this kind have little or no reason to encourage scientists to attempt to identify and develop theoretical alternatives that are radically distinct from (...)
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  8. The Epistemic Importance of Establishing the Absence of an Effect.Ari Kruger, Fiona Fidler, Felix Singleton Thorn, Ashley Barnett & Steven Kambouris - 2018 - Advances in Methods and Practices in Psychological Science 1 (2):237-244.
  9. Kokeellinen yhteiskuntatiede.Jaakko Kuorikoski & Samuli Reijula - 2018 - In Tuukka Kaidesoja, Tomi Kankainen & Petri Ylikoski (eds.), Syistä selityksiin. Kausaalisuus ja selittäminen yhteiskuntatieteissä. pp. 279-307.
    Tässä luvussa tarkastelemme hypoteesien testaamista ja kokeellista kausaalista järkeilyä tieteenfilosofisesta näkökulmasta. Arvioimme kokeellisen menetelmän mahdollisuuksia ja rajoituksia yhteiskuntatieteellisen tutkimuksen kontekstissa, jossa luonnontieteille ominaisia yleispäteviä teorioita harvoin on saatavilla ja jossa suoraviivaisiin kausaaliväitteisiin suhtaudutaan usein epäillen. Tämä luku ei siis ole menetelmäopas, joka kädestä pitäen opastaisi, kuinka yhteiskuntatieteellisiä kokeita tulisi rakentaa, vaan katsaus niihin perustaviin metodologisiin kysymyksiin ja periaatteisiin, joihin varsinaiset menetelmät nojaavat.
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  10. Significance Testing, P-Values and the Principle of Total Evidence.Bengt Autzen - 2016 - European Journal for Philosophy of Science 6 (2):281-295.
    The paper examines the claim that significance testing violates the Principle of Total Evidence. I argue that p-values violate PTE for two-sided tests but satisfy PTE for one-sided tests invoking a sufficient test statistic independent of the preferred theory of evidence. While the focus of the paper is to evaluate a particular claim about the relationship of significance testing and PTE, I clarify the reading of this methodological principle along the way.
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  11. Enhancing the Use of Decision Support Systems for Re-Engineering of Operations and Business- Applied Study on the Palestinian Universities.Samy S. Abu Naser & Mazen J. Al Shobaki - 2016 - Journal of Multidisciplinary Engineering Science Studies 2 (5):505--512.
    This research aims to identify the use of decision support systems as an entry point for operations of re-engineering in the Palestinian universities in Gaza Strip. The researchers used the method of questionnaire to collect data, and the researchers used a sample stratified random way, were (350) questionnaire distributed on the research sample and (312) questionnaire were collected back (89.1%). The study results showed that the most important ones are: there exists statistically significant impact at the level of significance (α (...)
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  12. Hypothesis Testing, “Dutch Book” Arguments, and Risk.Daniel Malinsky - 2015 - Philosophy of Science 82 (5):917-929.
    “Dutch Book” arguments and references to gambling theorems are typical in the debate between Bayesians and scientists committed to “classical” statistical methods. These arguments have rarely convinced non-Bayesian scientists to abandon certain conventional practices, partially because many scientists feel that gambling theorems have little relevance to their research activities. In other words, scientists “don’t bet.” This article examines one attempt, by Schervish, Seidenfeld, and Kadane, to progress beyond such apparent stalemates by connecting “Dutch Book”–type mathematical results with principles actually endorsed (...)
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  13. The Epistemology of the Precautionary Principle: Two Puzzles Resolved.Asbjørn Steglich-Petersen - 2015 - Erkenntnis 80 (5):1013-1021.
    In a recent paper in this journal, Carter and Peterson raise two distinctly epistemological puzzles that arise for anyone aspiring to defend the precautionary principle. The first puzzle trades on an application of epistemic contextualism to the precautionary principle; the second puzzle concerns the compatibility of the precautionary principle with the de minimis rule. In this note, I argue that neither puzzle should worry defenders of the precautionary principle. The first puzzle can be shown to be an instance of the (...)
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  14. Why Do Funding Agencies Favor Hypothesis Testing?Chris Haufe - 2013 - Studies in History and Philosophy of Science Part A 44 (3):363-374.
    Exploratory inquiry has difficulty attracting research funding because funding agencies have little sense of how to detect good science in exploratory contexts. After documenting and explaining the focus on hypothesis testing among a variety of institutions responsible for distinguishing between good and bad science, I analyze the NIH grant review process. I argue that a good explanation for the focus on hypothesis testing—at least at the level of science funding agencies—is the fact that hypothesis-driven research is relatively easy to appraise. (...)
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  15. New Tools for Theory Choice and Theory Diagosis.John R. Welch - 2013 - Studies in History and Philosophy of Science Part A 44 (3):318-329.
    Theory choice can be approached in at least four ways. One of these calls for the application of decision theory, and this article endorses this approach. But applying standard forms of decision theory imposes an overly demanding standard of numeric information, supposedly satisfied by point-valued utility and probability functions. To ameliorate this difficulty, a version of decision theory that requires merely comparative utilities and plausibilities is proposed. After a brief summary of this alternative, the article illustrates how comparative decision theory (...)
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  16. The Utility of Knowledge.Campbell Brown - 2012 - Erkenntnis 77 (2):155-65.
    Recent epistemology has introduced a new criterion of adequacy for analyses of knowledge: such an analysis, to be adequate, must be compatible with the common view that knowledge is better than true belief. One account which is widely thought to fail this test is reliabilism, according to which, roughly, knowledge is true belief formed by reliable process. Reliabilism fails, so the argument goes, because of the "swamping problem". In brief, provided a belief is true, we do not care whether or (...)
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  17. An Improper Introduction to Epistemic Utility Theory.Richard Pettigrew - 2011 - In Henk de Regt, Samir Okasha & Stephan Hartmann (eds.), Proceedings of EPSA: Amsterdam '09. Springer. pp. 287--301.
    Beliefs come in different strengths. What are the norms that govern these strengths of belief? Let an agent's belief function at a particular time be the function that assigns, to each of the propositions about which she has an opinion, the strength of her belief in that proposition at that time. Traditionally, philosophers have claimed that an agent's belief function at any time ought to be a probability function (Probabilism), and that she ought to update her belief function upon obtaining (...)
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  18. The Operationalization of General Hypotheses Versus the Discovery of Empirical Laws in Psychology.Stéphane Vautier - 2011 - Philosophia Scientae 15:105-122.
  19. Bayesian Probability.Patrick Maher - 2010 - Synthese 172 (1):119 - 127.
    Bayesian decision theory is here construed as explicating a particular concept of rational choice and Bayesian probability is taken to be the concept of probability used in that theory. Bayesian probability is usually identified with the agent’s degrees of belief but that interpretation makes Bayesian decision theory a poor explication of the relevant concept of rational choice. A satisfactory conception of Bayesian decision theory is obtained by taking Bayesian probability to be an explicatum for inductive probability given the agent’s evidence.
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  20. Learning From Error, Severe Testing, and the Growth of Theoretical Knowledge.Deborah G. Mayo - 2010 - In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. pp. 28.
  21. Can Scientific Theories Be Warranted with Severity? Exchanges with Alan Chalmers.Deborah G. Mayo - 2010 - In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press.
  22. The Discovery of Argon: A Case for Learning From Data?Aris Spanos - 2010 - Philosophy of Science 77 (3):359-380.
    Rayleigh and Ramsay discovered the inert gas argon in the atmospheric air in 1895 using a carefully designed sequence of experiments guided by an informal statistical analysis of the resulting data. The primary objective of this article is to revisit this remarkable historical episode in order to make a case that the error‐statistical perspective can be used to bring out and systematize (not to reconstruct) these scientists' resourceful ways and strategies for detecting and eliminating error, as well as dealing with (...)
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  23. Some Results in Bayesian Confirmation Theory with Applications.David Jehle - 2009 - Dissertation, Cornell University
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  24. How to Discount Double-Counting When It Counts: Some Clarifications.Deborah G. Mayo - 2008 - British Journal for the Philosophy of Science 59 (4):857-879.
    The issues of double-counting, use-constructing, and selection effects have long been the subject of debate in the philosophical as well as statistical literature. I have argued that it is the severity, stringency, or probativeness of the test—or lack of it—that should determine if a double-use of data is admissible. Hitchcock and Sober ([2004]) question whether this ‘severity criterion' can perform its intended job. I argue that their criticisms stem from a flawed interpretation of the severity criterion. Taking their criticism as (...)
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  25. The Error Statistical Philosopher as Normative Naturalist.Deborah Mayo & Jean Miller - 2008 - Synthese 163 (3):305 - 314.
    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, (...)
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  26. Evidence and Evolution: The Logic Behind the Science.Elliott Sober - 2008 - Cambridge University Press.
    How should the concept of evidence be understood? And how does the concept of evidence apply to the controversy about creationism as well as to work in evolutionary biology about natural selection and common ancestry? In this rich and wide-ranging book, Elliott Sober investigates general questions about probability and evidence and shows how the answers he develops to those questions apply to the specifics of evolutionary biology. Drawing on a set of fascinating examples, he analyzes whether claims about intelligent design (...)
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  27. Error-Statistical Elimination of Alternative Hypotheses.Kent Staley - 2008 - Synthese 163 (3):397 - 408.
    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 low-level empirical hypotheses and (...)
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  28. Evaluation of Theories.Ilkka Niiniluoto - 2007 - In Theo A. F. Kuipers (ed.), General Philosophy of Science. North Holland. pp. 175--217.
  29. Justifying Conditionalization: Conditionalization Maximizes Expected Epistemic Utility.Hilary Greaves & David Wallace - 2006 - Mind 115 (459):607-632.
    According to Bayesian epistemology, the epistemically rational agent updates her beliefs by conditionalization: that is, her posterior subjective probability after taking account of evidence X, pnew, is to be set equal to her prior conditional probability pold(·|X). Bayesians can be challenged to provide a justification for their claim that conditionalization is recommended by rationality—whence the normative force of the injunction to conditionalize? There are several existing justifications for conditionalization, but none directly addresses the idea that conditionalization will be epistemically rational (...)
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  30. Falsificationism Falsified.Sven Ove Hansson - 2006 - Foundations of Science 11 (3):275-286.
    A conceptual analysis of falsificationism is performed, in which the central falsificationist thesis is divided into several components. Furthermore, an empirical study of falsification in science is reported, based on the 70 scientific contributions that were published as articles in Nature in 2000. Only one of these articles conformed to the falsificationist recipe for successful science, namely the falsification of a hypothesis that is more accessible to falsification than to verification. It is argued that falsificationism relies on an incorrect view (...)
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  31. Models and Statistical Inference: The Controversy Between Fisher and Neyman–Pearson.Lenhard Johannes - 2006 - British Journal for the Philosophy of Science 57 (1):69.
  32. Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction.Deborah G. Mayo & Aris Spanos - 2006 - British Journal for the Philosophy of Science 57 (2):323-357.
    Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive inference as opposed to inductive behavior. We argue that the relevance of error probabilities (...)
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  33. Evidence as Passing Severe Tests: Highly Probable Versus Highly Probed Hypotheses.Deborah G. Mayo - 2005 - In P. Achinstein (ed.), Scientific Evidence: Philosophical Theories & Applications. The Johns Hopkins University Press. pp. 95--128.
  34. The Influence of Responsibility and Guilt on Naive Hypothesis-Testing.Dr Francesco Mancini & Amelia Gangemi - 2004 - Thinking and Reasoning 10 (3):289 – 320.
    Three experiments were used to investigate individuals' hypothesis-testing process as a function of moral perceived utilities , which in turn depend on perceived responsibility and fear of guilt. Moral perceived utilities are related to individuals' moral standards and specifically to people's attempt to face up to their own responsibilities, and to avoid feeling guilty of irresponsibility. The results showed that responsibility and fear of guilt in testing hypotheses involved a process defined as prudential mode , which entails focusing on and (...)
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  35. La Observación Científica En El Proceso de Contrastación de Hipótesis Y Teorías (Scientific Observation in the Process of Testing Hypotheses and Theories).Juan Vázquez - 2004 - Theoria 19 (1):77-95.
    En este trabajo se plantea, en primer lugar, la conveniencia de distinguir en el proceso de la contrastación empirica de hipótesis y teorías entre observación cientifíca y percepción y, en segundo lugar, se muestra como el munda procesado a través de la percepción se erige en base o soporte empírico del conocimiento científico. Una de las consecuencias del trabajo es que Ia tesis de “la carga teórica" de Ia observación ha sido mal planteada, al dar par sentado que esa carga (...)
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  36. Algorithmic Randomness in Empirical Data.James W. McAllister - 2003 - Studies in History and Philosophy of Science Part A 34 (3):633-646.
    According to a traditional view, scientific laws and theories constitute algorithmic compressions of empirical data sets collected from observations and measurements. This article defends the thesis that, to the contrary, empirical data sets are algorithmically incompressible. The reason is that individual data points are determined partly by perturbations, or causal factors that cannot be reduced to any pattern. If empirical data sets are incompressible, then they exhibit maximal algorithmic complexity, maximal entropy and zero redundancy. They are therefore maximally efficient carriers (...)
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  37. Scientific Discovery From the Perspective of Hypothesis Acceptance.Eric Martin & Daniel Osherson - 2002 - Proceedings of the Philosophy of Science Association 2002 (3):S331-S341.
    A model of inductive inquiry is defined within the context of first‐order logic. The model conceives of inquiry as a game between Nature and a scientist. To begin the game, a nonlogical vocabulary is agreed upon by the two players, along with a partition of a class of countable structures for that vocabulary. Next, Nature secretly chooses one structure from some cell of the partition. She then presents the scientist with a sequence of facts about the chosen structure. With each (...)
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  38. Deciding to Believe.Carl Ginet - 2001 - In Matthias Steup (ed.), Knowledge, Truth and Duty. Oxford University Press. pp. 63-76.
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  39. Experimental Practice and an Error Statistical Account of Evidence.Deborah G. Mayo - 2000 - Philosophy of Science 67 (3):207.
    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 in which a (...)
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  40. Use-Novelty, Severity, and a Systematic Neglect of Relevant Alternatives.Tetsuji Iseda - 1999 - Philosophy of Science 66 (3):413.
    This paper analyzes Deborah Mayo's recent criticism of use-novelty requirement. She claims that her severity criterion captures actual scientific practice better than use-novelty, and that use-novelty is not a necessary condition for severity. Even though certain cases in which evidence used for the construction of the hypothesis can test the hypothesis severely, I do not think that her severity criterion fits better with our intuition about good tests than use-novelty. I argue for this by showing a parallelism in terms of (...)
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  41. Testability.Elliott Sober - 1999 - Proceedings and Addresses of the American Philosophical Association 73 (2):47-76.
    That some propositions are testable, while others are not, was a fundamental idea in the philosophical program known as logical empiricism. That program is now widely thought to be defunct. Quine’s (1953) “Two Dogmas of Empiricism” and Hempel’s (1950) “Problems and Changes in the Empiricist Criterion of Meaning” are among its most notable epitaphs. Yet, as we know from Mark Twain’s comment on an obituary that he once had the pleasure of reading about himself, the report of a death can (...)
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  42. Bayes and Beyond.Geoffrey Hellman - 1997 - Philosophy of Science 64 (2):191-221.
    Several leading topics outstanding after John Earman's Bayes or Bust? are investigated further, with emphasis on the relevance of Bayesian explication in epistemology of science, despite certain limitations. (1) Dutch Book arguments are reformulated so that their independence from utility and preference in epistemic contexts is evident. (2) The Bayesian analysis of the Quine-Duhem problem is pursued; the phenomenon of a "protective belt" of auxiliary statements around reasonably successful theories is explicated. (3) The Bayesian approach to understanding the superiority of (...)
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  43. How About Bust? Factoring Explanatory Power Back Into Theory Evaluation.Larry Laudan - 1997 - Philosophy of Science 64 (2):306-316.
  44. Novelty, Severity, and History in the Testing of Hypotheses: The Case of the Top Quark.Kent W. Staley - 1996 - Philosophy of Science 63 (3):255.
    It is sometimes held that facts confirm a hypothesis only if they were not used in the construction of that hypothesis. This requirement of "use novelty" introduces a historical aspect into the assessment of evidence claims. I examine a methodological principle invoked by physicists in the experimental search for the top quark that bears a striking resemblance to this view. However, this principle is better understood, both historically and philosophically, in terms of the need to conduct a severe test than (...)
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  45. Diverse Tests on an Independent World.J. D. Trout - 1995 - Studies in History and Philosophy of Science Part A 26 (3):407-429.
  46. A Realist Theory of Empirical Testing Resolving the Theory-Ladenness/ Objectivity Debate.Shelby D. Hunt - 1994 - Philosophy of the Social Sciences 24 (2):133-158.
    This article explores whether theory-ladenness makes empirical testing an inse cure foundation for objectivity. Specifically, this article uses path diagrams as visual heuristics to assist in (1) developing a parsimonious representation of the traditional empiricist view of empirical testing, (2) showing how the "New Image" view ostensibly threatens the objectivity of science, (3) proposing a unified, realist theory of empirical testing, (4) developing a representation of the unified theory, (5) exploring several potential threats to objectivity, (6) discussing the proposed theory's (...)
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  47. Betting on Theories.Patrick Maher - 1993 - Cambridge University Press.
    This book is a major contribution to decision theory, focusing on the question of when it is rational to accept scientific theories. The author examines both Bayesian decision theory and confirmation theory, refining and elaborating the views of Ramsey and Savage. He argues that the most solid foundation for confirmation theory is to be found in decision theory, and he provides a decision-theoretic derivation of principles for how many probabilities should be revised over time. Professor Maher defines a notion of (...)
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  48. Dominance and the Disunity of Method: Solving the Problems of Innovation and Consensus.Rachel Laudan & Larry Laudan - 1989 - Philosophy of Science 56 (2):221-237.
    It is widely supposed that the scientists in any field use identical standards for evaluating theories. Without such unity of standards, consensus about scientific theories is supposedly unintelligible. However, the hypothesis of uniform standards can explain neither scientific disagreement nor scientific innovation. This paper seeks to show how the presumption of divergent standards (when linked to a hypothesis of dominance) can explain agreement, disagreement and innovation. By way of illustrating how a rational community with divergent standards can encourage innovation and (...)
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  49. Hypothesis Tests and Confidence Intervals in the Single Case.D. J. Johnstone - 1988 - British Journal for the Philosophy of Science 39 (3):353-360.
  50. Tests of Significance Following R. A. Fisher.D. J. Johnstone - 1987 - British Journal for the Philosophy of Science 38 (4):481-499.
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