Search results for 'Statistical inference' (try it on Scholar)

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  1. Stephanie Denison & Fei Xu (2010). Integrating Physical Constraints in Statistical Inference by 11-Month-Old Infants. Cognitive Science 34 (5):885-908.score: 240.0
    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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  2. 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: 210.0
    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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  3. 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: 204.0
    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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  4. 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: 180.0
    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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  5. Dr Siu L. Chow (2003). Some Meta-Theoretical Issues Relating to Statistical Inference. Philosophical Explorations.score: 180.0
    This paper is a reply to some comments made by Green (2002) on Chow’s (2002) critique of Wilkinson and Task Force's (1999) report on statistical inference. Issues raised are (a) the inappropriateness of accepting methodological prescription on authority, (ii) the vacuity of non-falsifiable theories, (iii) the need to distinguish between experiment and meta﷓experiment, and (iv) the probability foundation of the null﷓hypothesis significance﷓test procedure (NHSTP). This reply is intended to foster a better understanding of research methods in general, and (...)
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  6. Henry Kyburg (1974). The Logical Foundations of Statistical Inference. Reidel.score: 180.0
    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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  7. Miklós Rédei (1992). When Can Non-Commutative Statistical Inference Be Bayesian? International Studies in the Philosophy of Science 6 (2):129 – 132.score: 180.0
    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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  8. Fei Xu & Joshua B. Tenenbaum (2001). Rational Statistical Inference: A Critical Component for Word Learning. Behavioral and Brain Sciences 24 (6):1123-1124.score: 180.0
    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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  9. Rodney W. Benoist, Jean-Paul Marchand & Wolfgang Yourgrau (1977). Statistical Inference and Quantum Mechanical Measurement. Foundations of Physics 7 (11-12):827-833.score: 180.0
    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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  10. Miklós Rédei (1992). When Can Non‐Commutative Statistical Inference Be Bayesian? International Studies in the Philosophy of Science 6 (2):129-132.score: 180.0
    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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  11. Zoltan Dienes (2008). Understanding Psychology as a Science: An Introduction to Scientific and Statistical Inference. Palgrave Macmillan.score: 180.0
    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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  12. Katie Steele (2013). Persistent Experimenters, Stopping Rules, and Statistical Inference. Erkenntnis 78 (4):937-961.score: 174.0
    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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  13. Kent Staley, Can Error-Statistical Inference Function Securely?score: 174.0
    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. The choice (...)
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  14. Christopher D. Green (2002). Comment on Chow's "Issues in Statistical Inference". Philosophical Explorations.score: 156.0
    Contrary to Chow, Wilkinson's report, though more tentative than it might have been, is a reasoned and valuable contribution to psychological science. For those who are quite familiar with the details of statistical methods, it confirms much of what has been happening in the literature over the past few decades. For those who have not been keeping abreast of new developments on the statistical scene, it alerts them in a gentle way that there have been some important changes (...)
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  15. Dr Siu L. Chow (2002). Issues in Statistical Inference. Philosophical Explorations.score: 156.0
    The APA Task Force’s treatment of research methods is critically examined. The present defense of the experiment rests on showing that (a) the control group cannot be replaced by the contrast group, (b) experimental psychologists have valid reasons to use non-randomly selected subjects, (c) there is no evidential support for the experimenter expectancy effect, (d) the Task Force had misrepresented the role of inductive and deductive logic, and (e) the validity of experimental data does not require appealing to the effect (...)
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  16. Peffrey A. Witmer & Murray K. Clayton (1986). On Objectivity and Subjectivity in Statistical Inference: A Response to Mayo. Synthese 67 (2):369 - 379.score: 156.0
    In this paper we respond to the article An Objective Theory of Statistical Testing by D. G. Mayo (1983). We argue that the theory of testing developed by Mayo, NPT*, is neither novel nor objective. We also respond to the claims made by Mayo against Bayesian theory.
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  17. Henry E. Kyburg (1998). Logic and the Foundations of Statistical Inference. Behavioral and Brain Sciences 21 (2):208-209.score: 152.0
    The rapprochement between methodology and statistics suggested by Chow's book is a much needed one. His examples suggest that the situation is even worse in psychology than in some other disciplines. It is suggested that both historical accuracy and attention to recent work on the foundations of statistics would be beneficial in achieving the goals that Chow seeks.
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  18. Jan-Willem Romeijn (2005). Theory Change and Bayesian Statistical Inference. Philosophy of Science 72 (5):1174-1186.score: 150.0
  19. G. A. Barnard (1972). The Logic of Statistical Inference. British Journal for the Philosophy of Science 23 (2):123-132.score: 150.0
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  20. Rodney W. Benoist, Jean-Paul Marchand & Wolfgang Yourgrau (1978). Addendum to Statistical Inference and Quantum Mechanical Measurement. Foundations of Physics 8 (1-2):117-118.score: 150.0
  21. E. Jennifer Ashworth (1974). Probability and Statistical Inference in Ancient and Medieval Jewish Literature. By Nachum L. Rabinovitch. Toronto: University of Toronto Press. 1973. Pp. Xiii, 205. [REVIEW] Dialogue 13 (04):799-800.score: 150.0
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  22. J. D. Trout (1999). Measured Realism and Statistical Inference: An Explanation for the Fast Progress of "Hard" Psychology. Philosophy of Science 66 (3):272.score: 150.0
    The use of null hypothesis significance testing (NHST) in psychology has been under sustained attack, despite its reliable use in the notably successful, so-called "hard" areas of psychology, such as perception and cognition. I argue that, in contrast to merely methodological analyses of hypothesis testing (in terms of "test severity," or other confirmation-theoretic notions), only a patently metaphysical position can adequately capture the uneven but undeniable successes of theories in "hard psychology." I contend that Measured Realism satisfies this description, and (...)
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  23. Fei Xu & Susan Carey (2011). Rational Constructivism, Statistical Inference, and Core Cognition. Behavioral and Brain Sciences 34 (3):151.score: 150.0
    I make two points in this commentary on Carey (2009). First, it may be too soon to conclude that core cognition is innate. Recent advances in computational cognitive science and developmental psychology suggest possible mechanisms for developing inductive biases. Second, there is another possible answer to Fodor's challenge – if concepts are merely mental tokens, then cognitive scientists should spend their time on developing a theory of belief fixation instead.
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  24. G. A. Barnard (1972). Two Points in the Theory of Statistical Inference. British Journal for the Philosophy of Science 23 (4):329-331.score: 150.0
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  25. Howard Hua Yang, Noboru Murata & Shun-Ichi Amari (1998). Statistical Inference: Learning in Artificial Neural Networks. Trends in Cognitive Sciences 2 (1):4-10.score: 150.0
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  26. William S. Verplanck (1998). Statistical Inference: Why Wheels Spin. Behavioral and Brain Sciences 21 (2):223-224.score: 150.0
    NHSTP is embedded in the research of “cognitive science.” Its use is based on unstated assumptions about the practices of sampling, “operationalizing,” and using group data. NHSTP has facilitated both research and theorizing – research findings of limited interest – diverse theories that seldom complement one another. Alternative methods are available for data acquisition and analysis, and for assessing the “truth- value” of generalizations.
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  27. Bernd I. Dahn (1978). Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science. Studia Logica 37 (2):213-219.score: 150.0
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  28. Richard C. Jeffrey (1969). Statistical Explanation Vs. Statistical Inference. In Nicholas Rescher (ed.), Essays in Honor of Carl G. Hempel. Reidel. 104--113.score: 150.0
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  29. Alex C. Michalos (1967). Logic of Statistical Inference. By Ian Hacking. Cambridge University Press; Toronto: Macmillan of Canada, 1965. Pp. Ix, 227. $6.75. [REVIEW] Dialogue 5 (04):647-649.score: 150.0
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  30. Richard Buxton (1978). The Interpretation and Justification of the Subjective Bayesian Approach to Statistical Inference. British Journal for the Philosophy of Science 29 (1):25-38.score: 150.0
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  31. B. C. (1982). Philosophical Problems of Statistical Inference. Review of Metaphysics 35 (4):907-909.score: 150.0
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  32. John M. Vickers (1976). The Emergence of Probability: A Philosophical Study of Early Ideas About Probability, Induction and Statistical Inference (Review). Journal of the History of Philosophy 14 (3):366-367.score: 150.0
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  33. Lucas N. H. Bunt (1967). Probability and Statistical Inference in the Secondary School. Dialectica 21 (1‐4):366-382.score: 150.0
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  34. Rolando Chuaqui (1991). Truth, Possibility and Probability: New Logical Foundations of Probability and Statistical Inference Vol. 166. Access Online Via Elsevier.score: 150.0
    This unique book presents a new interpretation of probability, rooted in the traditional interpretation that was current in the 17th and 18th centuries.
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  35. Clark Glymour, David Madigan, Daniel Pregibon & Padhraic Smyth, Statistical Inference and Data Mining.score: 150.0
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  36. Lenhard Johannes (2006). Models and Statistical Inference: The Controversy Between Fisher and Neyman-Pearson. British Journal for the Philosophy of Science 57 (1):69.score: 150.0
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  37. J. M. B. Moss (1966). Logic of Statistical Inference. Philosophical Books 7 (3):12-15.score: 150.0
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  38. Jan Sprenger (2010). Statistical Inference Without Frequentist Justifications. In M. Dorato M. Suàrez (ed.), Epsa Epistemology and Methodology of Science. Springer. 289--297.score: 150.0
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  39. Fei Xu (2007). Rational Statistical Inference and Cognitive Development. In Peter Carruthers (ed.), The Innate Mind: Foundations and the Future. Oxford University Press, Usa. 3--199.score: 150.0
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  40. Fei Xu & Stephanie Denison (2009). Statistical Inference and Sensitivity to Sampling in 11-Month-Old Infants. Cognition 112 (1):97-104.score: 150.0
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  41. Desmond Bell (1987). The Emergence of Probability: A Philosophical Study of Early Ideas About Probability, Induction and Statistical Inference. History of European Ideas 8 (6):745-747.score: 150.0
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  42. Clark Glymour (2009). Causation and Statistical Inference. In Helen Beebee, Christopher Hitchcock & Peter Menzies (eds.), The Oxford Handbook of Causation. Oup Oxford.score: 150.0
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  43. W. Hooker, C., Harper (ed.) (1976). Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science.score: 150.0
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  44. Richard C. Jeffrey (1972). Review: Gerhard Tintner, Foundations of Probability and Statistical Inference; G. A. Barnard, F. J. Anscombe, Bartlett, W. Perks, J. R. N. Stone, J. O. Irwin, G. Tintner, Discussion. [REVIEW] Journal of Symbolic Logic 37 (3):630-631.score: 150.0
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  45. B. Kent Parker & Charles P. Shimp (1991). Intuitive Statistical Inference: An “Irrational” Context Effect in College Students' Categorization of Binomial Samples. Bulletin of the Psychonomic Society 29 (5):411-414.score: 150.0
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  46. Patricia Lovie (1985). A Note on an Unexpected Anchoring Bias in Intuitive Statistical Inference. Cognition 21 (1):69-72.score: 150.0
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  47. Gordon Matthews (1975). Review: Ian Hacking, Logic of Statistical Inference. [REVIEW] Journal of Symbolic Logic 40 (2):250-250.score: 150.0
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  48. J. J. McMahon (1967). Logic of Statistical Inference. Philosophical Studies 16:338-339.score: 150.0
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  49. Mikl (1992). When Can Non-Commutative Statistical Inference Be Bayesian? International Studies in the Philosophy of Science 6 (2):129 – 132.score: 150.0
     
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  50. John Murray, Marie Iding, Hilary Farris & Russ Revlin (1987). Sample-Size Salience and Statistical Inference. Bulletin of the Psychonomic Society 25 (5):367-369.score: 150.0
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