25 found
Order:
See also
Aris Spanos
Virginia Tech
Aris Spanos
Virginia Tech
  1.  92
    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 (...)
    Direct download (10 more)  
     
    Export citation  
     
    Bookmark   25 citations  
  2.  59
    Methodology in Practice: Statistical Misspecification Testing.Deborah G. Mayo & Aris Spanos - 2004 - Philosophy of Science 71 (5):1007-1025.
    The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve around whether the assumptions are actually met in the studies available, e.g., in psychology, ecology, biology, risk assessment. Philosophical scrutiny can help disentangle 'practical' problems of model validation, and conversely, (...)
    Direct download (8 more)  
     
    Export citation  
     
    Bookmark   26 citations  
  3.  74
    Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science.Deborah G. Mayo & Aris Spanos (eds.) - 2009 - Cambridge University Press.
    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 (...)
    Direct download  
     
    Export citation  
     
    Bookmark   6 citations  
  4.  79
    Who Should Be Afraid of the Jeffreys-Lindley Paradox?Aris Spanos - 2013 - Philosophy of Science 80 (1):73-93.
  5. 1. Marr on Computational-Level Theories Marr on Computational-Level Theories (Pp. 477-500).Oron Shagrir, John D. Norton, Holger Andreas, Jouni-Matti Kuukkanen, Aris Spanos, Eckhart Arnold, Elliott Sober, Peter Gildenhuys & Adela Helena Roszkowski - 2010 - Philosophy of Science 77 (4).
  6.  45
    Is Frequentist Testing Vulnerable to the Base-Rate Fallacy?Aris Spanos - 2010 - Philosophy of Science 77 (4):565-583.
    This article calls into question the charge that frequentist testing is susceptible to the base-rate fallacy. It is argued that the apparent similarity between examples like the Harvard Medical School test and frequentist testing is highly misleading. A closer scrutiny reveals that such examples have none of the basic features of a proper frequentist test, such as legitimate data, hypotheses, test statistics, and sampling distributions. Indeed, the relevant error probabilities are replaced with the false positive/negative rates that constitute deductive calculations (...)
    Direct download (7 more)  
     
    Export citation  
     
    Bookmark   4 citations  
  7.  6
    Methodology in Practice: Statistical Misspecification Testing.Deborah G. Mayo & Aris Spanos - 2004 - Philosophy of Science 71 (5):1007-1025.
  8.  43
    Curve Fitting, the Reliability of Inductive Inference, and the Error‐Statistical Approach.Aris Spanos - 2007 - Philosophy of Science 74 (5):1046-1066.
    The main aim of this paper is to revisit the curve fitting problem using the reliability of inductive inference as a primary criterion for the ‘fittest' curve. Viewed from this perspective, it is argued that a crucial concern with the current framework for addressing the curve fitting problem is, on the one hand, the undue influence of the mathematical approximation perspective, and on the other, the insufficient attention paid to the statistical modeling aspects of the problem. Using goodness-of-fit as the (...)
    Direct download (7 more)  
     
    Export citation  
     
    Bookmark   4 citations  
  9.  16
    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 (...)
    Direct download (5 more)  
     
    Export citation  
     
    Bookmark   3 citations  
  10.  39
    Error Statistical Modeling and Inference: Where Methodology Meets Ontology.Aris Spanos & Deborah G. Mayo - 2015 - Synthese 192 (11):3533-3555.
    In empirical modeling, an important desiderata for deeming theoretical entities and processes as real is that they can be reproducible in a statistical sense. Current day crises regarding replicability in science intertwines with the question of how statistical methods link data to statistical and substantive theories and models. Different answers to this question have important methodological consequences for inference, which are intertwined with a contrast between the ontological commitments of the two types of models. The key to untangling them is (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  11.  44
    A Frequentist Interpretation of Probability for Model-Based Inductive Inference.Aris Spanos - 2013 - Synthese 190 (9):1555-1585.
    The main objective of the paper is to propose a frequentist interpretation of probability in the context of model-based induction, anchored on the Strong Law of Large Numbers (SLLN) and justifiable on empirical grounds. It is argued that the prevailing views in philosophy of science concerning induction and the frequentist interpretation of probability are unduly influenced by enumerative induction, and the von Mises rendering, both of which are at odds with frequentist model-based induction that dominates current practice. The differences between (...)
    Direct download (4 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  12.  28
    Revisiting the Omitted Variables Argument: Substantive Vs. Statistical Adequacy.Aris Spanos - 2006 - Journal of Economic Methodology 13 (2):179-218.
    The problem of omitted variables is commonly viewed as a statistical misspecification issue which renders the inference concerning the influence of X t on yt unreliable, due to the exclusion of certain relevant factors W t . That is, omitting certain potentially important factors W t may confound the influence of X t on yt . The textbook omitted variables argument attempts to assess the seriousness of this unreliability using the sensitivity of the estimator to the inclusion/exclusion of W t (...)
    Direct download (4 more)  
     
    Export citation  
     
    Bookmark   2 citations  
  13.  3
    Graphical Causal Modeling and Error Statistics : Exchanges with Clark Glymour.Aris Spanos - 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. 364.
    Direct download  
     
    Export citation  
     
    Bookmark   1 citation  
  14. Theory Testing in Economics and the Error-Statistical Perspective.Aris Spanos - 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, Cambridge. pp. 1-419.
     
    Export citation  
     
    Bookmark   1 citation  
  15. Review of ST Ziliak and DN McCloskey's The Cult of Statistical Significance. [REVIEW]Aris Spanos - 2008 - Erasmus Journal for Philosophy and Economics 1 (1):154-164.
  16.  26
    Revisiting Data Mining: 'Hunting' with or Without a License.Aris Spanos - 2000 - Journal of Economic Methodology 7 (2):231-264.
    The primary objective of this paper is to revisit a number of empirical modelling activities which are often characterized as data mining, in an attempt to distinguish between the problematic and the non-problematic cases. The key for this distinction is provided by the notion of error-statistical severity. It is argued that many unwarranted data mining activities often arise because of inherent weaknesses in the Traditional Textbook (TT) methodology. Using the Probabilistic Reduction (PR) approach to empirical modelling, it is argued that (...)
    Direct download (5 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  17.  2
    Stephen T. Ziliak and Deirdre N. McCloskey's The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives. Ann Arbor : The University of Michigan Press, 2008, Xxiii+322 Pp. [REVIEW]Aris Spanos - 2008 - Erasmus Journal for Philosophy and Economics 1 (1):154.
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  18. Foundational Issues in Statistical Modeling : Statistical Model Specification.Aris Spanos - 2011 - Rationality, Markets and Morals 2:146-178.
    Statistical model specification and validation raise crucial foundational problems whose pertinent resolution holds the key to learning from data by securing the reliability of frequentist inference. The paper questions the judiciousness of several current practices, including the theory-driven approach, and the Akaike-type model selection procedures, arguing that they often lead to unreliable inferences. This is primarily due to the fact that goodness-of-fit/prediction measures and other substantive and pragmatic criteria are of questionable value when the estimated model is statistically misspecified. Foisting (...)
     
    Export citation  
     
    Bookmark  
  19.  25
    Error in Economics and the Error Statistical Approach Error in Economics. Towards a More Evidence-Based Methodology , Julian Reiss, Routledge, 2007, XXIV + 246 Pages. [REVIEW]Aris Spanos - 2009 - Economics and Philosophy 25 (2):206-210.
  20.  13
    Error in Economics and the Error Statistical Approach.Aris Spanos - 2009 - Economics and Philosophy 25 (2):206.
  21.  5
    Revisiting Haavelmo's Structural Econometrics: Bridging the Gap Between Theory and Data.Aris Spanos - 2015 - Journal of Economic Methodology 22 (2):171-196.
    The objective of the paper is threefold. First, to argue that some of Haavelmo's methodological ideas and insights have been neglected because they are largely at odds with the traditional perspective that views empirical modeling in economics as an exercise in curve-fitting. Second, to make a case that this neglect has contributed to the unreliability of empirical evidence in economics that is largely due to statistical misspecification. The latter affects the reliability of inference by inducing discrepancies between the actual and (...)
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark  
  22.  2
    On a New Philosophy of Frequentist Inference : Exchanges with David Cox and Deborah G. Mayo.Aris Spanos - 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. 315.
  23.  1
    Review of Error in Economics. Towards a More Evidence-Based Methodology. [REVIEW]Aris Spanos - 2009 - Economics and Philosophy 25 (2):206-210.
  24. Introduction and Background.Deborah G. Mayo & Aris Spanos - 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.
    No categories
     
    Export citation  
     
    Bookmark  
  25. Stephen Ziliak and Deirdre McCloskey’s The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives. [REVIEW]Aris Spanos - 2008 - Erasmus Journal for Philosophy and Economics 1 (1):154-164.