This text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis.

This article shares reflections from members of the community of philosophers of education in the United States and Canada who were invited to express their insights in response to the theme ‘Snaps...

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 (...) is to ensure that only statistical hypotheses that have passed severe or probative tests are inferred from the data. The severity criterion supplies a meta-statistical principle for evaluating proposed statistical inferences, avoiding classic fallacies from tests that are overly sensitive, as well as those not sensitive enough to particular errors and discrepancies. Introduction and overview 1.1 Behavioristic and inferential rationales for Neyman–Pearson (N–P) tests 1.2 Severity rationale: induction as severe testing 1.3 Severity as a meta-statistical concept: three required restrictions on the N–P paradigm Error statistical tests from the severity perspective 2.1 N–P test T(): type I, II error probabilities and power 2.2 Specifying test T() using p-values Neyman's post-data use of power 3.1 Neyman: does failure to reject H warrant confirming H? Severe testing as a basic concept for an adequate post-data inference 4.1 The severity interpretation of acceptance (SIA) for test T() 4.2 The fallacy of acceptance (i.e., an insignificant difference): Ms Rosy 4.3 Severity and power Fallacy of rejection: statistical vs. substantive significance 5.1 Taking a rejection of H0 as evidence for a substantive claim or theory 5.2 A statistically significant difference from H0 may fail to indicate a substantively important magnitude 5.3 Principle for the severity interpretation of a rejection (SIR) 5.4 Comparing significant results with different sample sizes in T(): large n problem 5.5 General testing rules for T(), using the severe testing concept The severe testing concept and confidence intervals 6.1 Dualities between one and two-sided intervals and tests 6.2 Avoiding shortcomings of confidence intervals Beyond the N–P paradigm: pure significance, and misspecification tests Concluding comments: have we shown severity to be a basic concept in a N–P philosophy of induction? (shrink)

While many philosophers of science have accorded special evidential significance to tests whose results are "novel facts", there continues to be disagreement over both the definition of novelty and why it should matter. The view of novelty favored by Giere, Lakatos, Worrall and many others is that of use-novelty: An accordance between evidence e and hypothesis h provides a genuine test of h only if e is not used in h's construction. I argue that what lies behind the intuition that (...) novelty matters is the deeper intuition that severe tests matter. I set out a criterion of severity akin to the notion of a test's power in Neyman-Pearson statistics. I argue that tests which are use-novel may fail to be severe, and tests that are severe may fail to be use-novel. I discuss the 1919 eclipse data as a severe test of Einstein's law of gravity. (shrink)

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 (...) are challenged to reevaluate the assumptions of their own theories - philosophical or methodological. Practitioners may better appreciate the foundational issues around which their questions revolve and thereby become better 'applied philosophers'. Conversely, new avenues emerge for finally solving recalcitrant philosophical problems of induction, explanation and theory testing. (shrink)

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, (...) a methodology of statistical model validation can shed light on a number of issues of interest to philosophers of science. (shrink)

The key problem in the controversy over group selection is that of defining a criterion of group selection that identifies a distinct causal process that is irreducible to the causal process of individual selection. We aim to clarify this problem and to formulate an adequate model of irreducible group selection. We distinguish two types of group selection models, labeling them type I and type II models. Type I models are invoked to explain differences among groups in their respective rates of (...) production of contained individuals. Type II models are invoked to explain differences among groups in their respective rates of production of distinct new groups. Taking Elliott Sober's model as an exemplar, we argue that although type I models have some biological importance--they force biologists to consider the role of group properties in influencing the fitness of organisms--they fail to identify a distinct group-level causal selection process. Type II models if properly framed, however, do identify a group-level causal selection process that is not reducible to individual selection. We propose such a type II model and apply it to some of the major candidates for group selection. (shrink)

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 (...) new and more adequate philosophy of evidence can move. (shrink)

Paulo Freire : the educator, his oeuvre, and changing contexts -- Holistic interpretations of Freire's work : a critical review -- Critical literacy, praxis, and emancipatory politics -- "Remaining on the same side of the river" : neo-liberalism, party movements, and the struggle for greater coherence -- Reinventing Freire in a Southern context : the Mediterranean -- Engaging with practice : a Freirean reflection on different pedagogical sites.

Books on Paulo Freire continue to be published in abundance. It is often very difficult to separate the wheat from the chaff. Following on a great book on the subject by Walter Kohan, this book has...

I argue that the Bayesian Way of reconstructing Duhem's problem fails to advance a solution to the problem of which of a group of hypotheses ought to be rejected or "blamed" when experiment disagrees with prediction. But scientists do regularly tackle and often enough solve Duhemian problems. When they do, they employ a logic and methodology which may be called error statistics. I discuss the key properties of this approach which enable it to split off the task of testing auxiliary (...) hypotheses from that of appraising a primary hypothesis. By discriminating patterns of error, this approach can at least block, if not also severely test, attempted explanations of an anomaly. I illustrate how this approach directs progress with Duhemian problems and explains how scientists actually grapple with them. (shrink)

Suicide has been condemned in our culture in one way or another since Augustine offered theological arguments against it in the sixth century. More recently, theological condemnation has given way to the view that suicidal behavior must always be symptomatic of emotional disturbance and mental illness. However, suicide has not always been viewed so negatively. In other times and cultures, it has been held that circumstances might befall a person in which suicide would be a perfectly rational course of action, (...) in the same sense that any other course of action could be rational: that it could be sensible, i.e., defensible by good reasons, or that it could be in keeping with the agent's fundamental interests. Indiscriminate use of modern life-sustaining technologies has renewed interest in the possibility of rational suicide. Today proponents of rational suicide tend to equate the rationality of suicide with the competence of the decision to commit suicide. (shrink)

A common intuition about evidence is that if data x have been used to construct a hypothesis H, then x should not be used again in support of H. It is no surprise that x fits H, if H was deliberately constructed to accord with x. The question of when and why we should avoid such “double-counting” continues to be debated in philosophy and statistics. It arises as a prohibition against data mining, hunting for significance, tuning on the signal, and (...) ad hoc hypotheses, and as a preference for predesignated hypotheses and “surprising” predictions. I have argued that it is the severity or probativeness of the test—or lack of it—that should determine whether a double-use of data is admissible. I examine a number of surprising ambiguities and unexpected facts that continue to bedevil this debate. (shrink)

Antonio Gramsci is one of the major social and political theorists of the 20th century whose work has had an enormous influence on several fields, including educational theory and practice. Gramsci and Education demonstrates the relevance of Antonio Gramsci's thought for contemporary educational debates. The essays are written by scholars located in different parts of the world, a number of whom are well known internationally for their contributions to Gramscian scholarship and/or educational research. The collection deals with a broad range (...) of topics, including schooling, adult education in general, popular education, workers' education, cultural studies, critical pedagogy, multicultural education, and the role of intellectuals in contemporary society. (shrink)

After some general remarks about the interrelation between philosophical and statistical thinking, the discussion centres largely on significance tests. These are defined as the calculation of p-values rather than as formal procedures for ‘acceptance‘ and ‘rejection‘. A number of types of null hypothesis are described and a principle for evidential interpretation set out governing the implications of p- values in the specific circumstances of each application, as contrasted with a long-run interpretation. A number of more complicated situ- ations are discussed (...) in which modification of the simple p-value may be essential. (shrink)

Although physician‐assisted death can be a great benefit both to those who are terminally ill and those who are not, the risks for patients in these two categories are quite different. For now it is reasonable to make the benefit available only for those near death, and to await better evidence about the risks before making it more broadly available.

An important theme to have emerged from the new experimentalist movement is that much of actual scientific practice deals not with appraising full-blown theories but with the manifold local tasks required to arrive at data, distinguish fact from artifact, and estimate backgrounds. Still, no program for working out a philosophy of experiment based on this recognition has been demarcated. I suggest why the new experimentalism has come up short, and propose a remedy appealing to the practice of standard error statistics. (...) I illustrate a portion of my proposal using Galison's experimental narrative on neutral currents. (shrink)

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 (...) a springboard, I elucidate some of the central examples that have long been controversial, and clarify how the severity criterion is properly applied to them. Severity and Use-Constructing: Four Points (and Some Clarificatory Notes) 1.1 Point 1: Getting beyond ‘all or nothing’ standpoints 1.2 Point 2: The rationale for prohibiting double-counting is the requirement that tests be severe 1.3 Point 3: Evaluate severity of a test T by its associated construction rule R 1.4 Point 4: The ease of passing vs. ease of erroneous passing: Statistical vs. ‘Definitional’ probability The False Dilemma: Hitchcock and Sober 2.1 Marsha measures her desk reliably 2.2 A false dilemma Canonical Errors of Inference 3.1 How construction rules may alter the error-probing performance of tests 3.2 Rules for accounting for anomalies 3.3 Hunting for statistically significant differences Concluding Remarks. (shrink)

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 (...) the realization that behind every substantive model there is a statistical model that pertains exclusively to the probabilistic assumptions imposed on the data. It is not that the methodology determines whether to be a realist about entities and processes in a substantive field. It is rather that the substantive and statistical models refer to different entities and processes, and therefore call for different criteria of adequacy. (shrink)

This paper, rather than providing a comprehensive discussion around Paulo Freire’s ideas, focuses on one aspect of his body of work: colonialism. The emphasis is on the ‘oppressor consciousness’ and cultural invasion (seen in its broadest context to include institutional colonialism with special reference to the traditional, modernizing and prophetic church. It also deals with the complex issue of language in postcolonial contexts, with special reference to education in Guinea Bissau and its implications for other colonial contexts.

Liz JacksonEducation University of Hong KongNel Noddings is known around the world for her contributions to philosophy and philosophy of education. Her work on caring and relational ethics broke ne...

Based in a holistic exposition and appraisal of Gramsci’s writings that are of relevance to education in neoliberal times, this book--rather than simply applying Gramsci's theories to issues in education--argues that education constitutes the leitmotif of his entire oeuvre and lies at the heart of his conceptualization of the ancient Greek term hegemony that was used by other political theorists before him. Starting from this understanding, the book goes on to compare Gramsci's theories with those of later thinkers in the (...) development of a critical pedagogy that can confront neoliberalism in all its forms. (shrink)

Suicide is devastating. It is an assault on our ideas of what living is about. In Contemplating Suicide Gavin Fairbairn takes fresh look at suicidal self harm. His view is distinctive in not emphasising external facts: the presence or absence of a corpse, along with evidence that the person who has become a corpse, intended to do so. It emphasises the intentions that the person had in acting, rather than the consequences that follow from those actions. Much of the book (...) is devoted to an attempt to construct a natural history of suicidal self harm and to examine some of the ethical issues that it raises. Fairbairn sets his philosophical reflections against a background of practical experience in the caring professions and uses a storytelling approach in offering a critique of the current language of self harm along with some new ways of thinking. Among other things he offers cogent reasons for abandoning the mindless use of terms such as attempted suicide and parasuicide , and introduces a number of new terms including cosmic roulette , which he uses to describe a family of human acts in which people gamble with their lives. By elaborating a richer model of suicidal self harm than most philosophers and most practitioners of caring professions currently inhabit, Fairbairn has contributed to the development of understanding in this area. Among other things a richer model and vocabulary may reduce the likelihood that those who come into contact with suicidal self harm, will believe that familiarity with the physical facts of the matter - the actions of the suicider and the presence or absence of a corpse - is always sufficient to justify a definite conclusion about the nature of the self harming act. (shrink)

Once upon a time, logic was the philosopher’s tool for analyzing scientific reasoning. Nowadays, probability and statistics have largely replaced logic, and their most popular application—Bayesianism—has replaced the qualitative deductive relationship between a hypothesis h and evidence e with a quantitative measure of h’s probability in light of e.

Theories of statistical testing may be seen as attempts to provide systematic means for evaluating scientific conjectures on the basis of incomplete or inaccurate observational data. The Neyman-Pearson Theory of Testing (NPT) has purported to provide an objective means for testing statistical hypotheses corresponding to scientific claims. Despite their widespread use in science, methods of NPT have themselves been accused of failing to be objective; and the purported objectivity of scientific claims based upon NPT has been called into question. The (...) purpose of this paper is first to clarify this question by examining the conceptions of (I) the function served by NPT in science, and (II) the requirements of an objective theory of statistics upon which attacks on NPT's objectivity are based. Our grounds for rejecting these conceptions suggest altered conceptions of (I) and (II) that might avoid such attacks. Second, we propose a reformulation of NPT, denoted by NPT*, based on these altered conceptions, and argue that it provides an objective theory of statistics. The crux of our argument is that by being able to objectively control error frequencies NPT* is able to objectively evaluate what has or has not been learned from the result of a statistical test. (shrink)

While orthodox (Neyman-Pearson) statistical tests enjoy widespread use in science, the philosophical controversy over their appropriateness for obtaining scientific knowledge remains unresolved. I shall suggest an explanation and a resolution of this controversy. The source of the controversy, I argue, is that orthodox tests are typically interpreted as rules for making optimal decisions as to how to behave--where optimality is measured by the frequency of errors the test would commit in a long series of trials. Most philosophers of statistics, however, (...) view the task of statistical methods as providing appropriate measures of the evidential-strength that data affords hypotheses. Since tests appropriate for the behavioral-decision task fail to provide measures of evidential-strength, philosophers of statistics claim the use of orthodox tests in science is misleading and unjustified. What critics of orthodox tests overlook, I argue, is that the primary function of statistical tests in science is neither to decide how to behave nor to assign measures of evidential strength to hypotheses. Rather, tests provide a tool for using incomplete data to learn about the process that generated it. This they do, I show, by providing a standard for distinguishing differences (between observed and hypothesized results) due to accidental or trivial errors from those due to systematic or substantively important discrepancies. I propose a reinterpretation of a commonly used orthodox test to make this learning model of tests explicit. (shrink)

Kuhn maintains that what marks the transition to a science is the ability to carry out ‘normal’ science—a practice he characterizes as abandoning the kind of testing that Popper lauds as the hallmark of science. Examining Kuhn's own contrast with Popper, I propose to recast Kuhnian normal science. Thus recast, it is seen to consist of severe and reliable tests of low-level experimental hypotheses (normal tests) and is, indeed, the place to look to demarcate science. While thereby vindicating Kuhn on (...) demarcation, my recasting of normal science is seen to tell against Kuhn's view of revolutionary science. (shrink)

Cartwright argues for being a realist about theoretical entities but non-realist about theoretical laws. Her reason is that while the former involves causal explanation, the latter involves theoretical explanation; and inferences to causes, unlike inferences to theories, can avoid the redundancy objection--that one cannot rule out alternatives that explain the phenomena equally well. I sketch Cartwright's argument for inferring the most probable cause, focusing on Perrin's inference to molecular collisions as the cause of Brownian motion. I argue that either the (...) inference she describes fails to be a genuinely causal one, or else it too is open to the redundancy objection. However, I claim there is a way to sustain Cartwright's main insight: that it is possible to avoid the redundancy objection in certain cases of causal inference from experiments (e.g., Perrin). But, contrary to Cartwright, I argue that in those cases one is able to infer causes only by inferring some theoretical laws about how they produce experimental effects. (shrink)

En este trabajo se realiza una propuesta de comunicación transdisciplinar entre las ciencias cognitivas y la educación, tal y como se perfilan en el siglo XXI. En un primer lugar se contextualizan estos procesos de comunicación en el continuum histórico que transita desde los inicios del siglo pasado, con la configuración de la psicología educativa dentro del constructo de las ciencias educativas; pasando por la constitución de las ciencias cognitivas, a mediados del siglo XX que propició la emergencia de la (...) síntesis disciplinar entre las ciencias del cerebro y de la mente, para posibilitar un encuadre arraigado en el presente, y en las propuestas de consiliencia entre neurociencia cognitiva y educación. Desde este presente histórico, se efectúa una propuesta de diálogo entre las distintas epistemes (educativas y cognitivas), en relación con el fenómeno de la educación bilingüe, y en concreto en el contexto intercultural ecuatoriano. La integración de los distintos niveles de análisis que configuran un fenómeno complejo, como lo es todo fenómeno educativo, refleja cómo los intentos transdisciplinares orientados a reconocer la complejidad ontológica de un mismo objeto de estudio, requiere del diálogo entre las distintas epistemologías y metodologías implicadas. La apertura del campo educativo a las dimensiones mentales y cerebrales en su expresión cognitiva, requiere en este s. XXI procesos de apertura recíproca respecto de la influencia que el eje cultura-educación ejerce en el modelamiento de los procesos de cognición y aprendizaje. (shrink)

In this article, Cris Mayo examines the relationship among anti-LGBTQ policies, gay marriage, and sexuality education. Her concern is that because gay marriage is insufficiently different from heterosexual marriage, adding it as an issue to curriculum or broader culture debate elides rather than addresses sexual difference. In other words, marriage may be an assimilative aspiration that closes down discussions of what sexuality is and can mean, that sidesteps other related social issues such as health care for all, and that reinforces (...) sexuality and gender identity as privatized, not political, concerns. Mayo examines different strands of LGBTQ history that complicate the meanings of sexuality and that critique a variety of antigay or heterosexist policies for their exclusions. She concludes by suggesting that the possibilities of sexuality are not served by advocacy for one gay relationship formation and calling for a sexuality education that is instead directed at sexual diversity. (shrink)

There are two reasons, I claim, scientists do and should ignore standard philosophical theories of objective evidence: Such theories propose concepts that are far too weak to give scientists what they want from evidence, viz., a good reason to believe a hypothesis; and They provide concepts that make the evidential relationship a priori, whereas typically establishing an evidential claim requires empirical investigation.

A toast is due to one who slays Misguided followers of Bayes, And in their heart strikes fear and terror With probabilities of error! (E.L. Lehmann).

Bernard Mayo; VIII—Belief and Constraint, Proceedings of the Aristotelian Society, Volume 64, Issue 1, 1 June 1964, Pages 139–156, https://doi.org/10.1093/arist.

In this article, the authors highlight connections between research on Two Spirit Native Americans and standard social studies curriculum. Two Spirit is a Pan-Indian term describing Native Americans who believe they embody both masculine and feminine characteristics/traits in one physical body. Findingsfrom this research expand the field's conception of multiple perspectives and diversity, while creating opportunities for nuanced understandings of genderexpression and gender that go beyond the male/female dichotomy currently accepted as the norm. The authors utilize historical research and a (...) semi-structured,in-depth interview with a current Ojibwe leader to better understand and theorize Native Americans' acceptance of diverse ways of being. The authors conclude that modern-day social studies teachers and students have much to learn from this traditional Native worldview. Specifically, the authors believe that encouraging students to adopt more inclusive perspectives on gender expression and to recognize the plausibility of multiple genders may lead to more advanced, social learning. Combined with reinforcement from responsible adults at school, this recognition would lessen a variety of present-day social ills, including the incidence of teen suicide caused by homophobia. (shrink)

I document some of the main evidence showing that E. S. Pearson rejected the key features of the behavioral-decision philosophy that became associated with the Neyman-Pearson Theory of statistics (NPT). I argue that NPT principles arose not out of behavioral aims, where the concern is solely with behaving correctly sufficiently often in some long run, but out of the epistemological aim of learning about causes of experimental results (e.g., distinguishing genuine from spurious effects). The view Pearson did hold gives a (...) deeper understanding of NPT tests than their typical formulation as accept-reject routines, against which criticisms of NPT are really directed. The Pearsonian view that emerges suggests how NPT tests may avoid these criticisms while still retaining what is central to these methods: the control of error probabilities. (shrink)

The error statistical account of testing uses statistical considerations, not to provide a measure of probability of hypotheses, but to model patterns of irregularity that are useful for controlling, distinguishing, and learning from errors. The aim of this paper is (1) to explain the main points of contrast between the error statistical and the subjective Bayesian approach and (2) to elucidate the key errors that underlie the central objection raised by Colin Howson at our PSA 96 Symposium.