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Browsing by Author "Mayo, Deborah G."

Now showing 1 - 14 of 14
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    Animating the EPR-Experiment: Reasoning from Error in the Search for Bell Violations
    Vasudevan, Anubav (Virginia Tech, 2004-08-29)
    When faced with Duhemian problems of underdetermination, scientific method suggests neither a circumvention of such difficulties via the uncritical acceptance of background assumptions, nor the employment of epistemically unsatisfying subjectivist models of rational retainment. Instead, scientists are challenged to attack problems of underdetermination 'head-on', through a careful analysis of the severity of the testing procedures responsible for the production and modeling of their anomalous data. Researchers faced with the task of explaining empirical anomalies, employ a number of diverse and clever experimental techniques designed to cut through the Duhemian mists, and account for potential sources of error that might weaken an otherwise warranted inference. In lieu of such progressive experimental procedures, scientists try to identify the actual inferential work that an existing experiment is capable of providing so as to avoid ascribing to its output more discriminative power than it is rightfully due. We argue that the various strategies adopted by researchers involved in the testing of Bell's inequality, are well represented by Mayo's error-statistical notion of scientific evidence. In particular, an acceptance of her stringent demand for the output of severe tests to stand at the basis of rational inference, helps to explain the methodological reactions expressed by scientists in response to the loopholes that plagued the early Bell experiments performed by Alain Aspect et al.. At the same time, we argue as a counterpoint, that these very reactions present a challenge for 'top-down' approaches to Duhem's problem.
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    Experimental Knowledge in Cognitive Neuroscience: Evidence, Errors, and Inference
    Aktunc, Mahir Emrah (Virginia Tech, 2011-07-02)
    This is a work in the epistemology of functional neuroimaging (fNI) and it applies the error-statistical (ES) philosophy to inferential problems in fNI to formulate and address these problems. This gives us a clear, accurate, and more complete understanding of what we can learn from fNI and how we can learn it. I review the works in the epistemology of fNI which I group into two categories; the first category consists of discussions of the theoretical significance of fNI findings and the second category discusses methodological difficulties of fNI. Both types of works have shortcomings; the first category has been too theory-centered in its approach and the second category has implicitly or explicitly adopted the assumption that methodological difficulties of fNI cannot be satisfactorily addressed. In this dissertation, I address these shortcomings and show how and what kind of experimental knowledge fNI can reliably produce which would be theoretically significant. I take fMRI as a representative fNI procedure and discuss the history of its development. Two independent trajectories of research in physics and physiology eventually converge to give rise to fMRI. Thus, fMRI findings are laden in the theories of physics and physiology and I propose how this creates a kind of useful theory-ladenness which allows for the representation of and intervention in the constructs of cognitive neuroscience. Duhemian challenges and problems of underdetermination are often raised to argue that fNI is of little, if any, epistemic value for psychology. I show how the ES notions of severe tests and error probabilities can be applied in epistemological analyses of fMRI. The result is that hemodynamic hypotheses can be severely tested in fMRI experiments and I demonstrate how these hypotheses are theoretically significant and fuel the growth of experimental knowledge in cognitive neuroscience. Throughout this dissertation, I put the emphasis on the experimental knowledge we obtain from fNI and argue that this is the fruitful approach that enables us to see how fNI can contribute to psychology. In doing so, I offer an error-statistical epistemology of fNI, which hopefully will be a significant contribution to the philosophy of psychology.
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    Naturalism & Objectivity: Methods and Meta-methods
    Miller, Jean Anne (Virginia Tech, 2008-08-20)
    The error statistical account provides a basic account of evidence and inference. Formally, the approach is a re-interpretation of standard frequentist (Fisherian, Neyman-Pearson) statistics. Informally, it gives an account of inductive inference based on arguing from error, an analog of frequentist statistics, which keeps the concept of error probabilities central to the evaluation of inferences and evidence. Error statistical work at present tends to remain distinct from other approaches of naturalism and social epistemology in philosophy of science and, more generally, Science and Technology Studies (STS). My goal is to employ the error statistical program in order to address a number of problems to approaches in philosophy of science, which fall under two broad headings: (1) naturalistic philosophy of science and (2) social epistemology. The naturalistic approaches that I am interested in looking at seek to provide us with an account of scientific and meta-scientific methodologies that will avoid extreme skepticism, relativism and subjectivity and claim to teach us something about scientific inferences and evidence produced by experiments (broadly construed). I argue that these accounts fail to identify a satisfactory program for achieving those goals and; moreover, to the extent that they succeed it is by latching on to the more general principles and arguments from error statistics. In sum, I will apply the basic ideas from error statistics and use them to examine (and improve upon) an area to which they have not yet been applied, namely in assessing and pushing forward these interdisciplinary pursuits involving naturalistic philosophies of science that appeal to cognitive science, psychology, the scientific record and a variety of social epistemologies.
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    On the Birnbaum Argument for the Strong Likelihood Principle
    Mayo, Deborah G. (Statistical Science, 2014)
    An essential component of inference based on familiar frequentist notions, such as p-values, significance and confidence levels, is the relevant sampling distribution. This feature results in violations of a principle known as the strong likelihood principle (SLP), the focus of this paper. In particular, if outcomes x∗ and y∗ from experiments E1 and E2 (both with unknown parameter θ) have different probability models f1(·), f2(·), then even though f1(x∗; θ) = cf2(y∗; θ) for all θ, outcomes x∗ and y∗ may have different implications for an inference about θ. Although such violations stem from considering outcomes other than the one observed, we argue this does not require us to consider experiments other than the one performed to produce the data. David Cox [Ann. Math. Statist. 29 (1958) 357–372] proposes the Weak Conditionality Principle (WCP) to justify restricting the space of relevant repetitions. The WCP says that once it is known which Ei produced the measurement, the assessment should be in terms of the properties of Ei . The surprising upshot of Allan Birnbaum’s [J. Amer. Statist. Assoc. 57 (1962) 269–306] argument is that the SLP appears to follow from applying the WCP in the case of mixtures, and so uncontroversial a principle as sufficiency (SP). But this would preclude the use of sampling distributions. The goal of this article is to provide a new clarification and critique of Birnbaum’s argument. Although his argument purports that [(WCP and SP) entails SLP], we show how data may violate the SLP while holding both the WCP and SP. Such cases also refute [WCP entails SLP].
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    Reframing the reproducibility crisis: using an error-statistical account to inform the interpretation of replication results in psychological research
    Parker, Caitlin Grace (Virginia Tech, 2015-06-17)
    Experimental psychology is said to be having a reproducibility crisis, marked by a low rate of successful replication. Researchers attempting to respond to the problem lack a framework for consistently interpreting the results of statistical tests, as well as standards for judging the outcomes of replication studies. In this paper I introduce an error-statistical framework for addressing these issues. I demonstrate how the severity requirement (and the associated severity construal of test results) can be used to avoid fallacious inferences that are complicit in the perpetuation of unreliable results. Researchers, I argue, must probe for error beyond the statistical level if they want to support substantive hypotheses. I then suggest how severity reasoning can be used to address standing questions about the interpretation of replication results.
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    A Solution to "The Problem of Socrates" in Nietzsche's Thought: An Explanation of Nietzsche's Ambivalence Toward Socrates
    Evans, Daw-Nay N. R. Jr. (Virginia Tech, 2004-01-26)
    Nietzsche's view of Socrates has been studied at length by a number of scholars, and yet the accounts resulting from these studies, even when descriptively correct, have not given a full explanation of the relationship between the two philosophers. More specifically, they fail to clarify the proper connection between Nietzsche and Socrates in terms of fundamental aspects of Nietzsche's thought, especially in terms of his view of reason. The most influential interpretation of Nietzsche's relationship to Socrates comes from Kaufmann, who claims that Nietzsche's view of Socrates is one of pure admiration. More recently, scholars such as Nehamas have corrected Kaufmann's flawed interpretation. Although Nehamas has properly understood Nietzsche's view of Socrates to be one of ambivalence, his interpretation is wanting in that it provides only a partial explanation of this ambivalence. My argument will take the following form. I will first establish in Chapters 2-5 (A) Nietzsche's ambivalence toward Socrates. Then, independently of that discussion, I will reveal in Chapter 6 (B) his ambivalence toward reason. The strict parallelism between these two manifestations of ambivalence in Nietzsche will permit me to make the claim that (B) explains (A). By this analysis I will demonstrate that Nietzsche is not only positive and negative in his assessments of both Socrates and reason, but that he is ambivalent to both for the same reasons. More specifically, for Nietzsche, Socrates' emphasis upon dialectical reason as the one and only medium for attaining eudaimonia is ultimately nihilistic. It stands as a singular example of the variety of nihilistic practices that emphasize one perspective over all others; and to deny perspective, is, for Nietzsche, to deny life itself. Thus Nietzsche understands such practices, among which he includes Christianity, ethical objectivism, and Plato's metaphysics, as a misuse of reason. However, the appropriate use of reason involves experimenting with other modes of expression such as aphorisms, the performing arts, and poetry, which grant the individual as much moral and intellectual freedom as necessary so that they may affirm life in the manner they find most satisfying and rewarding. Hence, it is only through a thorough investigation of Nietzsche's view of reason that his ambivalence toward Socrates can be fully understood, namely, as a manifestation of his ambivalence to reason.
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    Statistical Calibration and Validation of a Homogeneous Ventilated Wall-Interference Correction Method for the National Transonic Facility
    Walker, Eric L. (Virginia Tech, 2005-10-07)
    Wind tunnel experiments will continue to be a primary source of validation data for many types of mathematical and computational models in the aerospace industry. The increased emphasis on accuracy of data acquired from these facilities requires understanding of the uncertainty of not only the measurement data but also any correction applied to the data. One of the largest and most critical corrections made to these data is due to wall interference. In an effort to understand the accuracy and suitability of these corrections, a statistical validation process for wall interference correction methods has been developed. This process is based on the use of independent cases which, after correction, are expected to produce the same result. Comparison of these independent cases with respect to the uncertainty in the correction process establishes a domain of applicability based on the capability of the method to provide reasonable corrections with respect to customer accuracy requirements. The statistical validation method was applied to the version of the Transonic Wall Interference Correction System (TWICS) recently implemented in the National Transonic Facility at NASA Langley Research Center. The TWICS code generates corrections for solid and slotted wall interference in the model pitch plane based on boundary pressure measurements. Before validation could be performed on this method, it was necessary to calibrate the ventilated wall boundary condition parameters. Discrimination comparisons are used to determine the most representative of three linear boundary condition models which have historically been used to represent longitudinally slotted test section walls. Of the three linear boundary condition models implemented for ventilated walls, the general slotted wall model was the most representative of the data. The TWICS code using the calibrated general slotted wall model was found to be valid to within the process uncertainty for test section Mach numbers less than or equal to 0.60. The scatter among the mean corrected results of the bodies of revolution validation cases was within one count of drag on a typical transport aircraft configuration for Mach numbers at or below 0.80 and two counts of drag for Mach numbers at or below 0.90.
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    Statistical Science and Philosophy of Science: Where Do/Should They Meet in 2011 (and Beyond)?
    Mayo, Deborah G. (RMM, 2011)
    Debates over the philosophical foundations of statistics have a long and fascinating history; the decline of a lively exchange between philosophers of science and statisticians is relatively recent. Is there something special about 2011 (and beyond) that calls for renewed engagement in these fields? I say yes. There are some surprising, pressing, and intriguing new philosophical twists on the long-running controversies that cry out for philosophical analysis, and I hope to galvanize my co-contributors as well as the reader to take up the general cause.
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    Statistical Science Meets Philosophy of Science Part 2: Shallow versus Deep Explorations
    Mayo, Deborah G. (RMM, 2012)
    Inability to clearly defend against the criticisms of frequentist methods has turned many a frequentist away from venturing into foundational battlegrounds. Conceding the distorted perspectives drawn from overly literal and radical expositions of what Fisher, Neyman, and Pearson ‘really thought’, some deny they matter to current practice. The goal of this paper is not merely to call attention to the howlers that pass as legitimate criticisms of frequentist error statistics, but also to sketch the main lines of an alternative statistical philosophy within which to better articulate the roles and value of frequentist tools.
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    Statistical significance and its critics: practicing damaging science, or damaging scientific practice?
    Mayo, Deborah G.; Hand, David (Springer, 2022-05-12)
    While the common procedure of statistical significance testing and its accompanying concept of p-values have long been surrounded by controversy, renewed concern has been triggered by the replication crisis in science. Many blame statistical significance tests themselves, and some regard them as sufficiently damaging to scientific practice as to warrant being abandoned. We take a contrary position, arguing that the central criticisms arise from misunderstanding and misusing the statistical tools, and that in fact the purported remedies themselves risk damaging science. We argue that banning the use of p-value thresholds in interpreting data does not diminish but rather exacerbates data-dredging and biasing selection effects. If an account cannot specify outcomes that will not be allowed to count as evidence for a claim-if all thresholds are abandoned-then there is no test of that claim. The contributions of this paper are: To explain the rival statistical philosophies underlying the ongoing controversy; To elucidate and reinterpret statistical significance tests, and explain how this reinterpretation ameliorates common misuses and misinterpretations; To argue why recent recommendations to replace, abandon, or retire statistical significance undermine a central function of statistics in science: to test whether observed patterns in the data are genuine or due to background variability.
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    Toward Error-Statistical Principles of Evidence in Statistical Inference
    Jinn, Nicole Mee-Hyaang (Virginia Tech, 2014-06-02)
    The context for this research is statistical inference, the process of making predictions or inferences about a population from observation and analyses of a sample. In this context, many researchers want to grasp what inferences can be made that are valid, in the sense of being able to uphold or justify by argument or evidence. Another pressing question among users of statistical methods is: how can spurious relationships be distinguished from genuine ones? Underlying both of these issues is the concept of evidence. In response to these (and similar) questions, two questions I work on in this essay are: (1) what is a genuine principle of evidence? and (2) do error probabilities have more than a long-run role? Concisely, I propose that felicitous genuine principles of evidence should provide concrete guidelines on precisely how to examine error probabilities, with respect to a test's aptitude for unmasking pertinent errors, which leads to establishing sound interpretations of results from statistical techniques. The starting point for my definition of genuine principles of evidence is Allan Birnbaum's confidence concept, an attempt to control misleading interpretations. However, Birnbaum's confidence concept is inadequate for interpreting statistical evidence, because using only pre-data error probabilities would not pick up on a test's ability to detect a discrepancy of interest (e.g., "even if the discrepancy exists" with respect to the actual outcome. Instead, I argue that Deborah Mayo's severity assessment is the most suitable characterization of evidence based on my definition of genuine principles of evidence.
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    U.S. Importation of French Cheeses: Trade Protectionism or Consumer Protection
    Goldstein, Samantha (Virginia Tech, 1999-07-28)
    This study examines the extent to which the equivalency provision presented in the SPS agreement is able to foster trade negotiations between countries adopting different food safety measures. The study examines the role of scientific evidence as well as the political, economic, and cultural factors in impacting the national regulatory process and the international trade negotiations. It focuses on the limitations of science in allowing countries to reach consensus in contentious trade-related debates laden with risk uncertainty and missing data. The study consists of comparing the key components of the U.S. and French regulatory systems to identify the cultural basis for the differences in the perception of listeria risk and in preferences to control it. The stringent standards adopted in the U.S. and the preference for pasteurization are attributed to the complete separation of the regulatory functions form those of food production, the open style of decision-making which allows private citizens to review and comment on administrative actions, the unwillingness of U.S. regulators to expose vulnerable individuals to deadly pathogens, and the reliance on quantitative data to validate the effectiveness of pasteurization. The more flexible standards impacting listeria regulation in France are attributed to the the integration of regulatory functions with those of food production, the consumer preference for natural products, the public's trust in the government's regulatory decisions, and the belief that the determination of appropriate safety measures should be left up to the producers.
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    The Utility of Mathematical Symbols
    Waters, John Michael (Virginia Tech, 2015-05-27)
    Explanations of why mathematics is useful to empirical research focus on mathematics' role as a representation or model. On platonist accounts, the representational relation is one of structural correspondence between features of the real world and the abstract mathematical structures that represent them. Where real numbers are concerned, however, there is good reason to think the world's correspondence with systems of real number symbols, rather than the real numbers themselves, can be utilized for our representational purposes. One way this can be accomplished is through a paraphrase interpretation of real number symbols where the symbols are taken to refer directly to the things in the world real numbers are supposed to represent. A platonist account of structural correspondence between structures of real numbers and the world can be found in the foundations of measurement where a scale of real numbers is applied to quantities of physical properties like length, mass and velocity. This subject will be employed as a demonstration of how abstract real numbers, traditionally construed as modeling features of the world, are superfluous if their symbols are taken to refer directly to those features.