D. Mayo: The Science Wars and the Statistics Wars: scientism, popular statist...jemille6
I will explore the extent to which concerns about ‘scientism’– an unwarranted obeisance to scientific over other methods of inquiry – are intertwined with issues in the foundations of the statistical data analyses on which (social, behavioral, medical and physical) science increasingly depends. The rise of big data, machine learning, and high-powered computer programs have extended statistical methods and modeling across the landscape of science, law and evidence-based policy, but this has been accompanied by enormous hand wringing as to the reliability, replicability, and valid use of statistics. Legitimate criticisms of scientism often stem from insufficiently self-critical uses of statistical methodology, broadly construed — i.e., from what might be called “statisticism”-- particularly when those methods are applied to matters of controversy.
Severe Testing: The Key to Error Correctionjemille6
D. G. Mayo's slides for her presentation given March 17, 2017 at Boston Colloquium for Philosophy of Science, Alfred I.Taub forum: "Understanding Reproducibility & Error Correction in Science"
Mayo: Evidence as Passing a Severe Test (How it Gets You Beyond the Statistic...jemille6
D. G. Mayo April 28, 2021 presentation to the CUNY Graduate Center Philosophy Colloquium "Evidence as Passing a Severe Test (How it Gets You Beyond the Statistics Wars)"
Replication Crises and the Statistics Wars: Hidden Controversiesjemille6
D. Mayo presentation at the X-Phil conference on "Reproducibility and Replicabililty in Psychology and Experimental Philosophy", University College London (June 14, 2018)
Deborah G. Mayo: Is the Philosophy of Probabilism an Obstacle to Statistical Fraud Busting?
Presentation slides for: Revisiting the Foundations of Statistics in the Era of Big Data: Scaling Up to Meet the Challenge[*] at the Boston Colloquium for Philosophy of Science (Feb 21, 2014).
D. Mayo: The Science Wars and the Statistics Wars: scientism, popular statist...jemille6
I will explore the extent to which concerns about ‘scientism’– an unwarranted obeisance to scientific over other methods of inquiry – are intertwined with issues in the foundations of the statistical data analyses on which (social, behavioral, medical and physical) science increasingly depends. The rise of big data, machine learning, and high-powered computer programs have extended statistical methods and modeling across the landscape of science, law and evidence-based policy, but this has been accompanied by enormous hand wringing as to the reliability, replicability, and valid use of statistics. Legitimate criticisms of scientism often stem from insufficiently self-critical uses of statistical methodology, broadly construed — i.e., from what might be called “statisticism”-- particularly when those methods are applied to matters of controversy.
Severe Testing: The Key to Error Correctionjemille6
D. G. Mayo's slides for her presentation given March 17, 2017 at Boston Colloquium for Philosophy of Science, Alfred I.Taub forum: "Understanding Reproducibility & Error Correction in Science"
Mayo: Evidence as Passing a Severe Test (How it Gets You Beyond the Statistic...jemille6
D. G. Mayo April 28, 2021 presentation to the CUNY Graduate Center Philosophy Colloquium "Evidence as Passing a Severe Test (How it Gets You Beyond the Statistics Wars)"
Replication Crises and the Statistics Wars: Hidden Controversiesjemille6
D. Mayo presentation at the X-Phil conference on "Reproducibility and Replicabililty in Psychology and Experimental Philosophy", University College London (June 14, 2018)
Deborah G. Mayo: Is the Philosophy of Probabilism an Obstacle to Statistical Fraud Busting?
Presentation slides for: Revisiting the Foundations of Statistics in the Era of Big Data: Scaling Up to Meet the Challenge[*] at the Boston Colloquium for Philosophy of Science (Feb 21, 2014).
Abstract: Mounting failures of replication in the social and biological sciences give a practical spin to statistical foundations in the form of the question: How can we attain reliability when methods make illicit cherry-picking and significance seeking so easy? Researchers, professional societies, and journals are increasingly getting serious about methodological reforms to restore scientific integrity – some are quite welcome (e.g., pre-registration), while others are quite radical. The American Statistical Association convened members from differing tribes of frequentists, Bayesians, and likelihoodists to codify misuses of P-values. Largely overlooked are the philosophical presuppositions of both criticisms and proposed reforms. Paradoxically, alternative replacement methods may enable rather than reveal illicit inferences due to cherry-picking, multiple testing, and other biasing selection effects. Crowd-sourced reproducibility research in psychology is helping to change the reward structure but has its own shortcomings. Focusing on purely statistical considerations, it tends to overlook problems with artificial experiments. Without a better understanding of the philosophical issues, we can expect the latest reforms to fail.
D. G. Mayo: Your data-driven claims must still be probed severelyjemille6
In the session "Philosophy of Science and the New Paradigm of Data-Driven Science at the American Statistical Association Conference on Statistical Learning and Data Science/Nonparametric Statistics
D. G. Mayo (Virginia Tech) "Error Statistical Control: Forfeit at your Peril" presented May 23 at the session on "The Philosophy of Statistics: Bayesianism, Frequentism and the Nature of Inference," 2015 APS Annual Convention in NYC.
Controversy Over the Significance Test Controversyjemille6
Deborah Mayo (Professor of Philosophy, Virginia Tech, Blacksburg, Virginia) in PSA 2016 Symposium: Philosophy of Statistics in the Age of Big Data and Replication Crises
Surrogate Science: How Fisher, Neyman-Pearson, and Bayes Were Transformed int...jemille6
Gerd Gigerenzer (Director of Max Planck Institute for Human Development, Berlin, Germany) in the PSA 2016 Symposium:Philosophy of Statistics in the Age of Big Data and Replication Crises
Fusion Confusion? Comments on Nancy Reid: "BFF Four-Are we Converging?"jemille6
D. Mayo's comments on Nancy Reid's "BFF Four-Are we Converging?" given May 2, 2017 at The Fourth Bayesian, Fiducial and Frequentists Workshop held at Harvard University.
Byrd statistical considerations of the histomorphometric test protocol (1)jemille6
"Statistical considerations of the histomorphometric test protocol"
John E. Byrd, Ph.D. D-ABFA
Maria-Teresa Tersigni-Tarrant, Ph.D.
Central Identification Laboratory
JPAC
Stephen Senn slides:"‘Repligate’: reproducibility in statistical studies. What does it mean and in what sense does it matter?" presented May 23 at the session on "The Philosophy of Statistics: Bayesianism, Frequentism and the Nature of Inference"," at the 2015 APS Annual Convention in NYC
"The Statistical Replication Crisis: Paradoxes and Scapegoats”jemille6
D. G. Mayo LSE Popper talk, May 10, 2016.
Abstract: Mounting failures of replication in the social and biological sciences give a practical spin to statistical foundations in the form of the question: How can we attain reliability when Big Data methods make illicit cherry-picking and significance seeking so easy? Researchers, professional societies, and journals are increasingly getting serious about methodological reforms to restore scientific integrity – some are quite welcome (e.g., preregistration), while others are quite radical. Recently, the American Statistical Association convened members from differing tribes of frequentists, Bayesians, and likelihoodists to codify misuses of P-values. Largely overlooked are the philosophical presuppositions of both criticisms and proposed reforms. Paradoxically, alternative replacement methods may enable rather than reveal illicit inferences due to cherry-picking, multiple testing, and other biasing selection effects. Popular appeals to “diagnostic testing” that aim to improve replication rates may (unintentionally) permit the howlers and cookbook statistics we are at pains to root out. Without a better understanding of the philosophical issues, we can expect the latest reforms to fail.
D. Mayo: Replication Research Under an Error Statistical Philosophy jemille6
D. Mayo (Virginia Tech) slides from her talk June 3 at the "Preconference Workshop on Replication in the Sciences" at the 2015 Society for Philosophy and Psychology meeting.
D. Mayo: Philosophical Interventions in the Statistics Warsjemille6
ABSTRACT: While statistics has a long history of passionate philosophical controversy, the last decade especially cries out for philosophical illumination. Misuses of statistics, Big Data dredging, and P-hacking make it easy to find statistically significant, but spurious, effects. This obstructs a test's ability to control the probability of erroneously inferring effects–i.e., to control error probabilities. Disagreements about statistical reforms reflect philosophical disagreements about the nature of statistical inference–including whether error probability control even matters! I describe my interventions in statistics in relation to three events. (1) In 2016 the American Statistical Association (ASA) met to craft principles for avoiding misinterpreting P-values. (2) In 2017, a "megateam" (including philosophers of science) proposed "redefining statistical significance," replacing the common threshold of P ≤ .05 with P ≤ .005. (3) In 2019, an editorial in the main ASA journal called for abandoning all P-value thresholds, and even the words "significant/significance".
A word on each. (1) Invited to be a "philosophical observer" at their meeting, I found the major issues were conceptual. P-values measure how incompatible data are from what is expected under a hypothesis that there is no genuine effect: the smaller the P-value, the more indication of incompatibility. The ASA list of familiar misinterpretations–P-values are not posterior probabilities, statistical significance is not substantive importance, no evidence against a hypothesis need not be evidence for it–I argue, should not be the basis for replacing tests with methods less able to assess and control erroneous interpretations of data. (Mayo 2016, 2019). (2) The "redefine statistical significance" movement appraises P-values from the perspective of a very different quantity: a comparative Bayes Factor. Failing to recognize how contrasting approaches measure different things, disputants often talk past each other (Mayo 2018). (3) To ban P-value thresholds, even to distinguish terrible from warranted evidence, I say, is a mistake (2019). It will not eradicate P-hacking, but it will make it harder to hold P-hackers accountable. A 2020 ASA Task Force on significance testing has just been announced. (I would like to think my blog errorstatistics.com helped.)
To enter the fray between rival statistical approaches, it helps to have a principle applicable to all accounts. There's poor evidence for a claim if little if anything has been done to find it flawed even if it is. This forms a basic requirement for evidence I call the severity requirement. A claim passes with severity only if it is subjected to and passes a test that probably would have found it flawed, if it were. It stems from Popper, though he never adequately cashed it out. A variant is the frequentist principle of evidence developed with Sir David Cox (Mayo and Cox 20
Exploratory Research is More Reliable Than Confirmatory Researchjemille6
PSA 2016 Symposium:
Philosophy of Statistics in the Age of Big Data and Replication Crises
Presenter: Clark Glymour (Alumni University Professor in Philosophy, Carnegie Mellon University, Pittsburgh, Pennsylvania)
ABSTRACT: Ioannidis (2005) argued that most published research is false, and that “exploratory” research in which many hypotheses are assessed automatically is especially likely to produce false positive relations. Colquhoun (2014) with simulations estimates that 30 to 40% of positive results using the conventional .05 cutoff for rejection of a null hypothesis is false. Their explanation is that true relationships in a domain are rare and the selection of hypotheses to test is roughly independent of their truth, so most relationships tested will in fact be false. Conventional use of hypothesis tests, in other words, suffers from a base rate fallacy. I will show that the reverse is true for modern search methods for causal relations because: a. each hypothesis is tested or assessed multiple times; b. the methods are biased against positive results; c. systems in which true relationships are rare are an advantage for these methods. I will substantiate the claim with both empirical data and with simulations of data from systems with a thousand to a million variables that result in fewer than 5% false positive relationships and in which 90% or more of the true relationships are recovered.
D. Mayo: Putting the brakes on the breakthrough: An informal look at the argu...jemille6
“Putting the Brakes on the Breakthrough, or ‘How I used simple logic to uncover a flaw in a controversial 50-year old ‘theorem’ in statistical foundations taken as a‘breakthrough’ in favor of Bayesian vs frequentist error statistics’”
Statistical skepticism: How to use significance tests effectively jemille6
Prof. D. Mayo, presentation Oct. 12, 2017 at the ASA Symposium on Statistical Inference : “A World Beyond p < .05” in the session: “What are the best uses for P-values?“
Slides given for Deborah G. Mayo talk at Minnesota Center for Philosophy of Science at University of Minnesota on the ASA 2016 statement on P-values and Error Statistics
D. Mayo: Philosophy of Statistics & the Replication Crisis in Sciencejemille6
D. Mayo discusses various disputes-notably the replication crisis in science-in the context of her just released book: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars.
Abstract: Mounting failures of replication in the social and biological sciences give a practical spin to statistical foundations in the form of the question: How can we attain reliability when methods make illicit cherry-picking and significance seeking so easy? Researchers, professional societies, and journals are increasingly getting serious about methodological reforms to restore scientific integrity – some are quite welcome (e.g., pre-registration), while others are quite radical. The American Statistical Association convened members from differing tribes of frequentists, Bayesians, and likelihoodists to codify misuses of P-values. Largely overlooked are the philosophical presuppositions of both criticisms and proposed reforms. Paradoxically, alternative replacement methods may enable rather than reveal illicit inferences due to cherry-picking, multiple testing, and other biasing selection effects. Crowd-sourced reproducibility research in psychology is helping to change the reward structure but has its own shortcomings. Focusing on purely statistical considerations, it tends to overlook problems with artificial experiments. Without a better understanding of the philosophical issues, we can expect the latest reforms to fail.
D. G. Mayo: Your data-driven claims must still be probed severelyjemille6
In the session "Philosophy of Science and the New Paradigm of Data-Driven Science at the American Statistical Association Conference on Statistical Learning and Data Science/Nonparametric Statistics
D. G. Mayo (Virginia Tech) "Error Statistical Control: Forfeit at your Peril" presented May 23 at the session on "The Philosophy of Statistics: Bayesianism, Frequentism and the Nature of Inference," 2015 APS Annual Convention in NYC.
Controversy Over the Significance Test Controversyjemille6
Deborah Mayo (Professor of Philosophy, Virginia Tech, Blacksburg, Virginia) in PSA 2016 Symposium: Philosophy of Statistics in the Age of Big Data and Replication Crises
Surrogate Science: How Fisher, Neyman-Pearson, and Bayes Were Transformed int...jemille6
Gerd Gigerenzer (Director of Max Planck Institute for Human Development, Berlin, Germany) in the PSA 2016 Symposium:Philosophy of Statistics in the Age of Big Data and Replication Crises
Fusion Confusion? Comments on Nancy Reid: "BFF Four-Are we Converging?"jemille6
D. Mayo's comments on Nancy Reid's "BFF Four-Are we Converging?" given May 2, 2017 at The Fourth Bayesian, Fiducial and Frequentists Workshop held at Harvard University.
Byrd statistical considerations of the histomorphometric test protocol (1)jemille6
"Statistical considerations of the histomorphometric test protocol"
John E. Byrd, Ph.D. D-ABFA
Maria-Teresa Tersigni-Tarrant, Ph.D.
Central Identification Laboratory
JPAC
Stephen Senn slides:"‘Repligate’: reproducibility in statistical studies. What does it mean and in what sense does it matter?" presented May 23 at the session on "The Philosophy of Statistics: Bayesianism, Frequentism and the Nature of Inference"," at the 2015 APS Annual Convention in NYC
"The Statistical Replication Crisis: Paradoxes and Scapegoats”jemille6
D. G. Mayo LSE Popper talk, May 10, 2016.
Abstract: Mounting failures of replication in the social and biological sciences give a practical spin to statistical foundations in the form of the question: How can we attain reliability when Big Data methods make illicit cherry-picking and significance seeking so easy? Researchers, professional societies, and journals are increasingly getting serious about methodological reforms to restore scientific integrity – some are quite welcome (e.g., preregistration), while others are quite radical. Recently, the American Statistical Association convened members from differing tribes of frequentists, Bayesians, and likelihoodists to codify misuses of P-values. Largely overlooked are the philosophical presuppositions of both criticisms and proposed reforms. Paradoxically, alternative replacement methods may enable rather than reveal illicit inferences due to cherry-picking, multiple testing, and other biasing selection effects. Popular appeals to “diagnostic testing” that aim to improve replication rates may (unintentionally) permit the howlers and cookbook statistics we are at pains to root out. Without a better understanding of the philosophical issues, we can expect the latest reforms to fail.
D. Mayo: Replication Research Under an Error Statistical Philosophy jemille6
D. Mayo (Virginia Tech) slides from her talk June 3 at the "Preconference Workshop on Replication in the Sciences" at the 2015 Society for Philosophy and Psychology meeting.
D. Mayo: Philosophical Interventions in the Statistics Warsjemille6
ABSTRACT: While statistics has a long history of passionate philosophical controversy, the last decade especially cries out for philosophical illumination. Misuses of statistics, Big Data dredging, and P-hacking make it easy to find statistically significant, but spurious, effects. This obstructs a test's ability to control the probability of erroneously inferring effects–i.e., to control error probabilities. Disagreements about statistical reforms reflect philosophical disagreements about the nature of statistical inference–including whether error probability control even matters! I describe my interventions in statistics in relation to three events. (1) In 2016 the American Statistical Association (ASA) met to craft principles for avoiding misinterpreting P-values. (2) In 2017, a "megateam" (including philosophers of science) proposed "redefining statistical significance," replacing the common threshold of P ≤ .05 with P ≤ .005. (3) In 2019, an editorial in the main ASA journal called for abandoning all P-value thresholds, and even the words "significant/significance".
A word on each. (1) Invited to be a "philosophical observer" at their meeting, I found the major issues were conceptual. P-values measure how incompatible data are from what is expected under a hypothesis that there is no genuine effect: the smaller the P-value, the more indication of incompatibility. The ASA list of familiar misinterpretations–P-values are not posterior probabilities, statistical significance is not substantive importance, no evidence against a hypothesis need not be evidence for it–I argue, should not be the basis for replacing tests with methods less able to assess and control erroneous interpretations of data. (Mayo 2016, 2019). (2) The "redefine statistical significance" movement appraises P-values from the perspective of a very different quantity: a comparative Bayes Factor. Failing to recognize how contrasting approaches measure different things, disputants often talk past each other (Mayo 2018). (3) To ban P-value thresholds, even to distinguish terrible from warranted evidence, I say, is a mistake (2019). It will not eradicate P-hacking, but it will make it harder to hold P-hackers accountable. A 2020 ASA Task Force on significance testing has just been announced. (I would like to think my blog errorstatistics.com helped.)
To enter the fray between rival statistical approaches, it helps to have a principle applicable to all accounts. There's poor evidence for a claim if little if anything has been done to find it flawed even if it is. This forms a basic requirement for evidence I call the severity requirement. A claim passes with severity only if it is subjected to and passes a test that probably would have found it flawed, if it were. It stems from Popper, though he never adequately cashed it out. A variant is the frequentist principle of evidence developed with Sir David Cox (Mayo and Cox 20
Exploratory Research is More Reliable Than Confirmatory Researchjemille6
PSA 2016 Symposium:
Philosophy of Statistics in the Age of Big Data and Replication Crises
Presenter: Clark Glymour (Alumni University Professor in Philosophy, Carnegie Mellon University, Pittsburgh, Pennsylvania)
ABSTRACT: Ioannidis (2005) argued that most published research is false, and that “exploratory” research in which many hypotheses are assessed automatically is especially likely to produce false positive relations. Colquhoun (2014) with simulations estimates that 30 to 40% of positive results using the conventional .05 cutoff for rejection of a null hypothesis is false. Their explanation is that true relationships in a domain are rare and the selection of hypotheses to test is roughly independent of their truth, so most relationships tested will in fact be false. Conventional use of hypothesis tests, in other words, suffers from a base rate fallacy. I will show that the reverse is true for modern search methods for causal relations because: a. each hypothesis is tested or assessed multiple times; b. the methods are biased against positive results; c. systems in which true relationships are rare are an advantage for these methods. I will substantiate the claim with both empirical data and with simulations of data from systems with a thousand to a million variables that result in fewer than 5% false positive relationships and in which 90% or more of the true relationships are recovered.
D. Mayo: Putting the brakes on the breakthrough: An informal look at the argu...jemille6
“Putting the Brakes on the Breakthrough, or ‘How I used simple logic to uncover a flaw in a controversial 50-year old ‘theorem’ in statistical foundations taken as a‘breakthrough’ in favor of Bayesian vs frequentist error statistics’”
Statistical skepticism: How to use significance tests effectively jemille6
Prof. D. Mayo, presentation Oct. 12, 2017 at the ASA Symposium on Statistical Inference : “A World Beyond p < .05” in the session: “What are the best uses for P-values?“
Slides given for Deborah G. Mayo talk at Minnesota Center for Philosophy of Science at University of Minnesota on the ASA 2016 statement on P-values and Error Statistics
D. Mayo: Philosophy of Statistics & the Replication Crisis in Sciencejemille6
D. Mayo discusses various disputes-notably the replication crisis in science-in the context of her just released book: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars.
D. G. Mayo: The Replication Crises and its Constructive Role in the Philosoph...jemille6
Constructive role of replication crises teaches a lot about 1.) Non-fallacious uses of statistical tests, 2.) Rationale for the role of probability in tests, 3.) How to reformulate tests.
The Statistics Wars: Errors and Casualtiesjemille6
ABSTRACT: Mounting failures of replication in the social and biological sciences give a new urgency to critically appraising proposed statistical reforms. While many reforms are welcome (preregistration of experiments, replication, discouraging cookbook uses of statistics), there have been casualties. The philosophical presuppositions behind the meta-research battles remain largely hidden. Too often the statistics wars have become proxy wars between competing tribe leaders, each keen to advance one or another tool or school, rather than build on efforts to do better science. Efforts of replication researchers and open science advocates are diminished when so much attention is centered on repeating hackneyed howlers of statistical significance tests (statistical significance isn’t substantive significance, no evidence against isn’t evidence for), when erroneous understanding of basic statistical terms goes uncorrected, and when bandwagon effects lead to popular reforms that downplay the importance of error probability control. These casualties threaten our ability to hold accountable the “experts,” the agencies, and all the data handlers increasingly exerting power over our lives.
“The importance of philosophy of science for statistical science and vice versa”jemille6
My paper “The importance of philosophy of science for statistical science and vice
versa” presented (zoom) at the conference: IS PHILOSOPHY USEFUL FOR SCIENCE, AND/OR VICE VERSA? January 30 - February 2, 2024 at Chapman University, Schmid College of Science and Technology.
Statistical Inference as Severe Testing: Beyond Performance and Probabilismjemille6
A talk given by Deborah G Mayo
(Dept of Philosophy, Virginia Tech) to the Seminar in Advanced Research Methods at the Dept of Psychology, Princeton University on
November 14, 2023
TITLE: Statistical Inference as Severe Testing: Beyond Probabilism and Performance
ABSTRACT: I develop a statistical philosophy in which error probabilities of methods may be used to evaluate and control the stringency or severity of tests. A claim is severely tested to the extent it has been subjected to and passes a test that probably would have found flaws, were they present. The severe-testing requirement leads to reformulating statistical significance tests to avoid familiar criticisms and abuses. While high-profile failures of replication in the social and biological sciences stem from biasing selection effects—data dredging, multiple testing, optional stopping—some reforms and proposed alternatives to statistical significance tests conflict with the error control that is required to satisfy severity. I discuss recent arguments to redefine, abandon, or replace statistical significance.
Paper given at PSA 22 Symposium: Multiplicity, Data-Dredging and Error Control
MAYO ABSTRACT: I put forward a general principle for evidence: an error-prone claim C is warranted to the extent it has been subjected to, and passes, an analysis that very probably would have found evidence of flaws in C just if they are present. This probability is the severity with which C has passed the test. When a test’s error probabilities quantify the capacity of tests to probe errors in C, I argue, they can be used to assess what has been learned from the data about C. A claim can be probable or even known to be true, yet poorly probed by the data and model at hand. The severe testing account leads to a reformulation of statistical significance tests: Moving away from a binary interpretation, we test several discrepancies from any reference hypothesis and report those well or poorly warranted. A probative test will generally involve combining several subsidiary tests, deliberately designed to unearth different flaws. The approach relates to confidence interval estimation, but, like confidence distributions (CD) (Thornton), a series of different confidence levels is considered. A 95% confidence interval method, say using the mean M of a random sample to estimate the population mean μ of a Normal distribution, will cover the true, but unknown, value of μ 95% of the time in a hypothetical series of applications. However, we cannot take .95 as the probability that a particular interval estimate (a ≤ μ ≤ b) is correct—at least not without a prior probability to μ. In the severity interpretation I propose, we can nevertheless give an inferential construal post-data, while still regarding μ as fixed. For example, there is good evidence μ ≥ a (the lower estimation limit) because if μ < a, then with high probability .95 (or .975 if viewed as one-sided) we would have observed a smaller value of M than we did. Likewise for inferring μ ≤ b. To understand a method’s capability to probe flaws in the case at hand, we cannot just consider the observed data, unlike in strict Bayesian accounts. We need to consider what the method would have inferred if other data had been observed. For each point μ’ in the interval, we assess how severely the claim μ > μ’ has been probed. I apply the severity account to the problems discussed by earlier speakers in our session. The problem with multiple testing (and selective reporting) when attempting to distinguish genuine effects from noise, is not merely that it would, if regularly applied, lead to inferences that were often wrong. Rather, it renders the method incapable, or practically so, of probing the relevant mistaken inference in the case at hand. In other cases, by contrast, (e.g., DNA matching) the searching can increase the test’s probative capacity. In this way the severe testing account can explain competing intuitions about multiplicity and data-dredging, while blocking inferences based on problematic data-dredging
Similar to Statistical Flukes, the Higgs Discovery, and 5 Sigma (20)
D. Mayo (Dept of Philosophy, VT)
Sir David Cox’s Statistical Philosophy and Its Relevance to Today’s Statistical Controversies
ABSTRACT: This talk will explain Sir David Cox's views of the nature and importance of statistical foundations and their relevance to today's controversies about statistical inference, particularly in using statistical significance testing and confidence intervals. Two key themes of Cox's statistical philosophy are: first, the importance of calibrating methods by considering their behavior in (actual or hypothetical) repeated sampling, and second, ensuring the calibration is relevant to the specific data and inquiry. A question that arises is: How can the frequentist calibration provide a genuinely epistemic assessment of what is learned from data? Building on our jointly written papers, Mayo and Cox (2006) and Cox and Mayo (2010), I will argue that relevant error probabilities may serve to assess how well-corroborated or severely tested statistical claims are.
Nancy Reid, Dept. of Statistics, University of Toronto. Inaugural receiptant of the "David R. Cox Foundations of Statistics Award".
Slides from Invited presentation at 2023 JSM: “The Importance of Foundations in Statistical Science“
Ronald Wasserstein, Chair (American Statistical Association)
ABSTRACT: David Cox wrote “A healthy interplay between theory and application is crucial for statistics… This is particularly the case when by theory we mean foundations of statistical analysis, rather than the theoretical analysis of specific statistical methods.” These foundations distinguish statistical science from the many fields of research in which statistical thinking is a key intellectual component. In this talk I will emphasize the ongoing importance and relevance of theoretical advances and theoretical thinking through some illustrative examples.
Errors of the Error Gatekeepers: The case of Statistical Significance 2016-2022jemille6
ABSTRACT: Statistical significance tests serve in gatekeeping against being fooled by randomness, but recent attempts to gatekeep these tools have themselves malfunctioned. Warranted gatekeepers formulate statistical tests so as to avoid fallacies and misuses of P-values. They highlight how multiplicity, optional stopping, and data-dredging can readily invalidate error probabilities. It is unwarranted, however, to argue that statistical significance and P-value thresholds be abandoned because they can be misused. Nor is it warranted to argue for abandoning statistical significance based on presuppositions about evidence and probability that are at odds with those underlying statistical significance tests. When statistical gatekeeping malfunctions, I argue, it undermines a central role to which scientists look to statistics. In order to combat the dangers of unthinking, bandwagon effects, statistical practitioners and consumers need to be in a position to critically evaluate the ramifications of proposed "reforms” (“stat activism”). I analyze what may be learned from three recent episodes of gatekeeping (and meta-gatekeeping) at the American Statistical Association (ASA).
Causal inference is not statistical inferencejemille6
Jon Williamson (University of Kent)
ABSTRACT: Many methods for testing causal claims are couched as statistical methods: e.g.,
randomised controlled trials, various kinds of observational study, meta-analysis, and
model-based approaches such as structural equation modelling and graphical causal
modelling. I argue that this is a mistake: causal inference is not a purely statistical
problem. When we look at causal inference from a general point of view, we see that
methods for causal inference fit into the framework of Evidential Pluralism: causal
inference is properly understood as requiring mechanistic inference in addition to
statistical inference.
Evidential Pluralism also offers a new perspective on the replication crisis. That
observed associations are not replicated by subsequent studies is a part of normal
science. A problem only arises when those associations are taken to establish causal
claims: a science whose established causal claims are constantly overturned is indeed
in crisis. However, if we understand causal inference as involving mechanistic inference
alongside statistical inference, as Evidential Pluralism suggests, we avoid fallacious
inferences from association to causation. Thus, Evidential Pluralism offers the means to
prevent the drama of science from turning into a crisis.
Stephan Guttinger (Lecturer in Philosophy of Data/Data Ethics, University of Exeter, UK)
ABSTRACT: The idea of “questionable research practices” (QRPs) is central to the narrative of a replication crisis in the experimental sciences. According to this narrative the low replicability of scientific findings is not simply due to fraud or incompetence, but in large part to the widespread use of QRPs, such as “p-hacking” or the lack of adequate experimental controls. The claim is that such flawed practices generate flawed output. The reduction – or even elimination – of QRPs is therefore one of the main strategies proposed by policymakers and scientists to tackle the replication crisis.
What counts as a QRP, however, is not clear. As I will discuss in the first part of this paper, there is no consensus on how to define the term, and ascriptions of the qualifier “questionable” often vary across disciplines, time, and even within single laboratories. This lack of clarity matters as it creates the risk of introducing methodological constraints that might create more harm than good. Practices labelled as ‘QRPs’ can be both beneficial and problematic for research practice and targeting them without a sound understanding of their dynamic and context-dependent nature risks creating unnecessary casualties in the fight for a more reliable scientific practice.
To start developing a more situated and dynamic picture of QRPs I will then turn my attention to a specific example of a dynamic QRP in the experimental life sciences, namely, the so-called “Far Western Blot” (FWB). The FWB is an experimental system that can be used to study protein-protein interactions but which for most of its existence has not seen a wide uptake in the community because it was seen as a QRP. This was mainly due to its (alleged) propensity to generate high levels of false positives and negatives. Interestingly, however, it seems that over the last few years the FWB slowly moved into the space of acceptable research practices. Analysing this shift and the reasons underlying it, I will argue a) that suppressing this practice deprived the research community of a powerful experimental tool and b) that the original judgment of the FWB was based on a simplistic and non-empirical assessment of its error-generating potential. Ultimately, it seems like the key QRP at work in the FWB case was the way in which the label “questionable” was assigned in the first place. I will argue that findings from this case can be extended to other QRPs in the experimental life sciences and that they point to a larger issue with how researchers judge the error-potential of new research practices.
David Hand (Professor Emeritus and Senior Research Investigator, Department of Mathematics,
Faculty of Natural Sciences, Imperial College London.)
ABSTRACT: Science progresses through an iterative process of formulating theories and comparing
them with empirical real-world data. Different camps of scientists will favour different
theories, until accumulating evidence renders one or more untenable. Not unnaturally,
people become attached to theories. Perhaps they invented a theory, and kudos arises
from being the originator of a generally accepted theory. A theory might represent a
life's work, so that being found wanting might be interpreted as failure. Perhaps
researchers were trained in a particular school, and acknowledging its shortcomings is
difficult. Because of this, tensions can arise between proponents of different theories.
The discipline of statistics is susceptible to precisely the same tensions. Here, however,
the tensions are not between different theories of "what is", but between different
strategies for shedding light on the real world from limited empirical data. This can be in
the form of how one measures discrepancy between the theory's predictions and
observations. It can be in the form of different ways of looking at empirical results. It can
be, at a higher level, because of differences between what is regarded as important in a
particular context. Or it can be for other reasons.
Perhaps the most familiar example of this tension within statistics is between different
approaches to inference. However, there are many other examples of such tensions.
This paper illustrates with several examples. We argue that the tension generally arises
as a consequence of inadequate care being taken in question formulation. That is,
insufficient thought is given to deciding exactly what one wants to know - to determining
"What is the question?".
The ideas and disagreements are illustrated with several examples.
The neglected importance of complexity in statistics and Metasciencejemille6
Daniele Fanelli
London School of Economics Fellow in Quantitative Methodology, Department of
Methodology, London School of Economics and Political Science.
ABSTRACT: Statistics is at war, and Metascience is ailing. This is partially due, the talk will argue, to
a paradigmatic blind-spot: the assumption that one can draw general conclusions about
empirical findings without considering the role played by context, conditions,
assumptions, and the complexity of methods and theories. Whilst ideally these
particularities should be unimportant in science, in practice they cannot be neglected in
most research fields, let alone in research-on-research.
This neglected importance of complexity is supported by theoretical arguments and
empirical findings (or the lack thereof) in the recent meta-analytical and metascientific
literature. The talk will overview this background and suggest how the complexity of
theories and methodologies may be explicitly factored into particular methodologies of
statistics and Metaresearch. The talk will then give examples of how this approach may
usefully complement existing paradigms, by translating results, methods and theories
into quantities of information that are evaluated using an information-compression logic.
Mathematically Elegant Answers to Research Questions No One is Asking (meta-a...jemille6
Uri Simonsohn (Professor, Department of Operations, Innovation and Data Sciences at Esade)
ABSTRACT: The statistical tools listed in the title share that a mathematically elegant solution has
become the consensus advice of statisticians, methodologists and some
mathematically sophisticated researchers writing tutorials and textbooks, and yet,
they lead research workers to meaningless answers, that are often also statistically
invalid. Part of the problem is that advice givers take the mathematical abstractions
of the tools they advocate for literally, instead of taking the actual behavior of
researchers seriously.
On Severity, the Weight of Evidence, and the Relationship Between the Twojemille6
Margherita Harris
Visiting fellow in the Department of Philosophy, Logic and Scientific Method at the London
School of Economics and Political Science.
ABSTRACT: According to the severe tester, one is justified in declaring to have evidence in support of a
hypothesis just in case the hypothesis in question has passed a severe test, one that it would be very
unlikely to pass so well if the hypothesis were false. Deborah Mayo (2018) calls this the strong
severity principle. The Bayesian, however, can declare to have evidence for a hypothesis despite not
having done anything to test it severely. The core reason for this has to do with the
(infamous) likelihood principle, whose violation is not an option for anyone who subscribes to the
Bayesian paradigm. Although the Bayesian is largely unmoved by the incompatibility between
the strong severity principle and the likelihood principle, I will argue that the Bayesian’s never-ending
quest to account for yet an other notion, one that is often attributed to Keynes (1921) and that is
usually referred to as the weight of evidence, betrays the Bayesian’s confidence in the likelihood
principle after all. Indeed, I will argue that the weight of evidence and severity may be thought of as
two (very different) sides of the same coin: they are two unrelated notions, but what brings them
together is the fact that they both make trouble for the likelihood principle, a principle at the core of
Bayesian inference. I will relate this conclusion to current debates on how to best conceptualise
uncertainty by the IPCC in particular. I will argue that failure to fully grasp the limitations of an
epistemology that envisions the role of probability to be that of quantifying the degree of belief to
assign to a hypothesis given the available evidence can be (and has been) detrimental to an
adequate communication of uncertainty.
Revisiting the Two Cultures in Statistical Modeling and Inference as they rel...jemille6
Aris Spanos (Wilson Schmidt Professor of Economics, Virginia Tech)
ABSTRACT: The discussion places the two cultures, the model-driven statistical modeling and the
algorithm-driven modeling associated with Machine Learning (ML) and Statistical
Learning Theory (SLT) in a broader context of paradigm shifts in 20th-century statistics,
which includes Fisher’s model-based induction of the 1920s and variations/extensions
thereof, the Data Science (ML, STL, etc.) and the Graphical Causal modeling in the
1990s. The primary objective is to compare and contrast the effectiveness of different
approaches to statistics in learning from data about phenomena of interest and relate
that to the current discussions pertaining to the statistics wars and their potential
casualties.
Comparing Frequentists and Bayesian Control of Multiple Testingjemille6
James Berger
ABSTRACT: A problem that is common to many sciences is that of having to deal with a multiplicity of statistical inferences. For instance, in GWAS (Genome Wide Association Studies), an experiment might consider 20 diseases and 100,000 genes, and conduct statistical tests of the 20x100,000=2,000,000 null hypotheses that a specific disease is associated with a specific gene. The issue is that selective reporting of only the ‘highly significant’ results could lead to many claimed disease/gene associations that turn out to be false, simply because of statistical randomness. In 2007, the seriousness of this problem was recognized in GWAS and extremely stringent standards were employed to resolve it. Indeed, it was recommended that tests for association should be conducted at an error probability of 5 x 10—7. Particle physicists similarly learned that a discovery would be reliably replicated only if the p-value of the relevant test was less than 5.7 x 10—7. This was because they had to account for a huge number of multiplicities in their analyses. Other sciences have continuing issues with multiplicity. In the Social Sciences, p-hacking and data dredging are common, which involve multiple analyses of data. Stopping rules in social sciences are often ignored, even though it has been known since 1933 that, if one keeps collecting data and computing the p-value, one is guaranteed to obtain a p-value less than 0.05 (or, indeed, any specified value), even if the null hypothesis is true. In medical studies that occur with strong oversight (e.g., by the FDA), control for multiplicity is mandated. There is also typically a large amount of replication, resulting in meta-analysis. But there are many situations where multiplicity is not handled well, such as subgroup analysis: one first tests for an overall treatment effect in the population; failing to find that, one tests for an effect among men or among women; failing to find that, one tests for an effect among old men or young men, or among old women or young women; …. I will argue that there is a single method that can address any such problems of multiplicity: Bayesian analysis, with the multiplicity being addressed through choice of prior probabilities of hypotheses. ... There are, of course, also frequentist error approaches (such as Bonferroni and FDR) for handling multiplicity of statistical inferences; indeed, these are much more familiar than the Bayesian approach. These are, however, targeted solutions for specific classes of problems and are not easily generalizable to new problems.
Clark Glamour
ABSTRACT: "Data dredging"--searching non experimental data for causal and other relationships and taking that same data to be evidence for those relationships--was historically common in the natural sciences--the works of Kepler, Cannizzaro and Mendeleev are examples. Nowadays, "data dredging"--using data to bring hypotheses into consideration and regarding that same data as evidence bearing on their truth or falsity--is widely denounced by both philosophical and statistical methodologists. Notwithstanding, "data dredging" is routinely practiced in the human sciences using "traditional" methods--various forms of regression for example. The main thesis of my talk is that, in the spirit and letter of Mayo's and Spanos’ notion of severe testing, modern computational algorithms that search data for causal relations severely test their resulting models in the process of "constructing" them. My claim is that in many investigations, principled computerized search is invaluable for reliable, generalizable, informative, scientific inquiry. The possible failures of traditional search methods for causal relations, multiple regression for example, are easily demonstrated by simulation in cases where even the earliest consistent graphical model search algorithms succeed. ... These and other examples raise a number of issues about using multiple hypothesis tests in strategies for severe testing, notably, the interpretation of standard errors and confidence levels as error probabilities when the structures assumed in parameter estimation are uncertain. Commonly used regression methods, I will argue, are bad data dredging methods that do not severely, or appropriately, test their results. I argue that various traditional and proposed methodological norms, including pre-specification of experimental outcomes and error probabilities for regression estimates of causal effects, are unnecessary or illusory in application. Statistics wants a number, or at least an interval, to express a normative virtue, the value of data as evidence for a hypothesis, how well the data pushes us toward the true or away from the false. Good when you can get it, but there are many circumstances where you have evidence but there is no number or interval to express it other than phony numbers with no logical connection with truth guidance. Kepler, Darwin, Cannizarro, Mendeleev had no such numbers, but they severely tested their claims by combining data dredging with severe testing.
The Duality of Parameters and the Duality of Probabilityjemille6
Suzanne Thornton
ABSTRACT: Under any inferential paradigm, statistical inference is connected to the logic of probability. Well-known debates among these various paradigms emerge from conflicting views on the notion of probability. One dominant view understands the logic of probability as a representation of variability (frequentism), and another prominent view understands probability as a measurement of belief (Bayesianism). The first camp generally describes model parameters as fixed values, whereas the second camp views parameters as random. Just as calibration (Reid and Cox 2015, “On Some Principles of Statistical Inference,” International Statistical Review 83(2), 293-308)--the behavior of a procedure under hypothetical repetition--bypasses the need for different versions of probability, I propose that an inferential approach based on confidence distributions (CD), which I will explain, bypasses the analogous conflicting perspectives on parameters. Frequentist inference is connected to the logic of probability through the notion of empirical randomness. Sample estimates are useful only insofar as one has a sense of the extent to which the estimator may vary from one random sample to another. The bounds of a confidence interval are thus particular observations of a random variable, where the randomness is inherited by the random sampling of the data. For example, 95% confidence intervals for parameter θ can be calculated for any random sample from a Normal N(θ, 1) distribution. With repeated sampling, approximately 95% of these intervals are guaranteed to yield an interval covering the fixed value of θ. Bayesian inference produces a probability distribution for the different values of a particular parameter. However, the quality of this distribution is difficult to assess without invoking an appeal to the notion of repeated performance. ... In contrast to a posterior distribution, a CD is not a probabilistic statement about the parameter, rather it is a data-dependent estimate for a fixed parameter for which a particular behavioral property holds. The Normal distribution itself, centered around the observed average of the data (e.g. average recovery times), can be a CD for θ. It can give any level of confidence. Such estimators can be derived through Bayesian or frequentist inductive procedures, and any CD, regardless of how it is obtained, guarantees performance of the estimator under replication for a fixed target, while simultaneously producing a random estimate for the possible values of θ.
The Statistics Wars and Their Causalities (refs)jemille6
High-profile failures of replication in the social and biological sciences underwrite a
minimal requirement of evidence: If little or nothing has been done to rule out flaws in inferring a claim, then it has not passed a severe test. A claim is severely tested to the extent it has been subjected to and passes a test that probably would have found flaws, were they present. This probability is the severity with which a claim has passed. The goal of highly well-tested claims differs from that of highly probable ones, explaining why experts so often disagree about statistical reforms. Even where today’s statistical test critics see themselves as merely objecting to misuses and misinterpretations, the reforms they recommend often grow out of presuppositions about the role of probability in inductive-statistical inference. Paradoxically, I will argue, some of the reforms intended to replace or improve on statistical significance tests enable rather than reveal illicit inferences due to cherry-picking, multiple testing, and data-dredging. Some preclude testing and falsifying claims altogether. These are the “casualties” on which I will focus. I will consider Fisherian vs Neyman-Pearson tests, Bayes factors, Bayesian posteriors, likelihoodist assessments, and the “screening model” of tests (a quasiBayesian-frequentist assessment). Whether one accepts this philosophy of evidence, I argue, that it provides a standpoint for avoiding both the fallacies of statistical testing and the casualties of today’s statistics wars.
The Statistics Wars and Their Casualties (w/refs)jemille6
High-profile failures of replication in the social and biological sciences underwrite a minimal requirement of evidence: If little or nothing has been done to rule out flaws in inferring a claim, then it has not passed a severe test. A claim is severely tested to the extent it has been subjected to and passes a test that probably would have found flaws, were they present. This probability is the severity with which a claim has passed. The goal of highly well-tested claims differs from that of highly probable ones, explaining why experts so often disagree about statistical reforms. Even where today’s statistical test critics see themselves as merely objecting to misuses and misinterpretations, the reforms they recommend often grow out of presuppositions about the role of probability in inductive-statistical inference. Paradoxically, I will argue, some of the reforms intended to replace or improve on statistical significance tests enable rather than reveal illicit inferences due to cherry-picking, multiple testing, and data-dredging. Some preclude testing and falsifying claims altogether. These are the “casualties” on which I will focus. I will consider Fisherian vs Neyman-Pearson tests, Bayes factors, Bayesian posteriors, likelihoodist assessments, and the “screening model” of tests (a quasi-Bayesian-frequentist assessment). Whether one accepts this philosophy of evidence, I argue, that it provides a standpoint for avoiding both the fallacies of statistical testing and the casualties of today’s statistics wars.
On the interpretation of the mathematical characteristics of statistical test...jemille6
Statistical hypothesis tests are often misused and misinterpreted. Here I focus on one
source of such misinterpretation, namely an inappropriate notion regarding what the
mathematical theory of tests implies, and does not imply, when it comes to the
application of tests in practice. The view taken here is that it is helpful and instructive to be consciously aware of the essential difference between mathematical model and
reality, and to appreciate the mathematical model and its implications as a tool for
thinking rather than something that has a truth value regarding reality. Insights are presented regarding the role of model assumptions, unbiasedness and the alternative hypothesis, Neyman-Pearson optimality, multiple and data dependent testing.
The role of background assumptions in severity appraisal (jemille6
In the past decade discussions around the reproducibility of scientific findings have led to a re-appreciation of the importance of guaranteeing claims are severely tested. The inflation of Type 1 error rates due to flexibility in the data analysis is widely considered
one of the underlying causes of low replicability rates. Solutions, such as study preregistration, are becoming increasingly popular to combat this problem. Preregistration only allows researchers to evaluate the severity of a test, but not all
preregistered studies provide a severe test of a claim. The appraisal of the severity of a
test depends on background information, such as assumptions about the data generating process, and auxiliary hypotheses that influence the final choice for the
design of the test. In this article, I will discuss the difference between subjective and
inter-subjectively testable assumptions underlying scientific claims, and the importance
of separating the two. I will stress the role of justifications in statistical inferences, the
conditional nature of scientific conclusions following these justifications, and highlight
how severe tests could lead to inter-subjective agreement, based on a philosophical approach grounded in methodological falsificationism. Appreciating the role of background assumptions in the appraisal of severity should shed light on current discussions about the role of preregistration, interpreting the results of replication studies, and proposals to reform statistical inferences.
The two statistical cornerstones of replicability: addressing selective infer...jemille6
Tukey’s last published work in 2020 was an obscure entry on multiple comparisons in the
Encyclopedia of Behavioral Sciences, addressing the two topics in the title. Replicability
was not mentioned at all, nor was any other connection made between the two topics. I shall demonstrate how these two topics critically affect replicability using recently completed studies. I shall review how these have been addressed in the past. I shall
review in more detail the available ways to address selective inference. My conclusion is that conducting many small replicability studies without strict standardization is the way to assure replicability of results in science, and we should introduce policies to make this happen.
The replication crisis: are P-values the problem and are Bayes factors the so...jemille6
Today’s posterior is tomorrow’s prior. Dennis Lindley
It has been claimed that science is undergoing a replication crisis and that when looking for culprits, the cult of significance is the chief suspect. It has also been claimed that Bayes factors might provide a solution.
In my opinion, these claims are misleading and part of the problem is our understanding
of the purpose and nature of replication, which has only recently been subject to formal
analysis.
What we are or should be interested in is truth. Replication is a coherence not a correspondence requirement and one that has a strong dependence on the size
of the replication study
.
Consideration of Bayes factors raises a puzzling question. Should the Bayes factor for a replication study be calculated as if it were the initial study? If the answer is yes, the approach is not fully Bayesian and furthermore the Bayes factors will be subject to
exactly the same replication ‘paradox’ as P-values. If the answer is no, then in what
sense can an initially found Bayes factor be replicated and what are the implications for how we should view replication of P-values?
A further issue is that little attention has been paid to false negatives and, by extension
to true negative values. Yet, as is well known from the theory of diagnostic tests, it is
meaningless to consider the performance of a test in terms of false positives alone.
I shall argue that we are in danger of confusing evidence with the conclusions we draw and that any reforms of scientific practice should concentrate on producing evidence
that is reliable as it can be qua evidence. There are many basic scientific practices in
need of reform. Pseudoreplication, for example, and the routine destruction of
information through dichotomisation are far more serious problems than many matters of inferential framing that seem to have excited statisticians.
High-profile failures of replication in the social and biological sciences underwrite a
minimal requirement of evidence: If little or nothing has been done to rule out flaws in
inferring a claim, then it has not passed a severe test. A claim is severely tested to the
extent it has been subjected to and passes a test that probably would have found flaws,
were they present. This probability is the severity with which a claim has passed. The
goal of highly well-tested claims differs from that of highly probable ones, explaining
why experts so often disagree about statistical reforms. Even where today’s statistical
test critics see themselves as merely objecting to misuses and misinterpretations, the
reforms they recommend often grow out of presuppositions about the role of probability
in inductive-statistical inference. Paradoxically, I will argue, some of the reforms
intended to replace or improve on statistical significance tests enable rather than reveal
illicit inferences due to cherry-picking, multiple testing, and data-dredging. Some
preclude testing and falsifying claims altogether. These are the “casualties” on which I
will focus. I will consider Fisherian vs Neyman-Pearson tests, Bayes factors, Bayesian
posteriors, likelihoodist assessments, and the “screening model” of tests (a quasiBayesian-frequentist assessment). Whether one accepts this philosophy of evidence, I
argue, that it provides a standpoint for avoiding both the fallacies of statistical testing
and the casualties of today’s statistics wars.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Statistical Flukes, the Higgs Discovery, and 5 Sigma
1. 11/5
1
Statistical Flukes, the Higgs Discovery,
and 5 Sigma
Deborah G. Mayo
Virginia Tech
(I) “5 sigma observed effect”.
One of the biggest science events of 2012-13 was the
announcement on July 4, 2012 of evidence for the discovery of
a Higgs particle based on a “5 sigma observed effect”.
With the March 2013 data analysis, the 5 sigma difference
grew to 7 sigmas.
2. 11/5
2
• Because the 5 sigma report refers to frequentist statistical
tests, the discovery was immediately imbued with
controversies from philosophy of statistics
• I’m an outsider to high energy physics, HEP, but (aside from
finding it fascinating), any philosopher of statistics worth her
salt should be able to illuminate some of the more public
controversies e.g., P-values.
Not difficult to do, fortunately.
3. 11/5
(II) Bad Science? (O’Hagan, prompted by Lindley)
To the ISBA: “Dear Bayesians: We’ve heard a lot about the
Higgs boson. ...Specifically, the news referred to a
confidence interval with 5-sigma limits.… Five standard
deviations, assuming normality, means a p-value of around
0.0000005…
Why such an extreme evidence requirement? We know from
a Bayesian perspective that this only makes sense if (a) the
existence of the Higgs boson has extremely small prior
probability and/or (b) the consequences of erroneously
announcing its discovery are dire in the extreme. …
…. Are the particle physics community completely wedded
to frequentist analysis? If so, has anyone tried to explain
what bad science that is?”
3
4. 11/5
4
Not bad science at all!
• HEP physicists are sophisticated with their statistical
methodology: they’d seen too many bumps disappear.
• They want to ensure that before announcing the
hypothesis H*: “a new particle has been discovered”
that:
H* has been given a severe run for its money.
Significance tests and cognate methods (confidence
intervals) are methods of choice here for good reason
5. 11/5
5
(III) Simple statistical significance test: ingredients
(i) Null or test hypothesis: in terms of an unknown parameter
μ in a statistical model, an idealized representation of
underlying data generation: a model of the detector
μ is the “global signal strength” parameter
H0: μ = 0 i.e., zero signal (background only hypothesis)
Η0: μ = 0 vs. Η1: μ > 0
μ = 1: Standard Model (SM) Higgs boson signal in addition to
the background
6. 11/5
6
Empirical data are modeled as observed values of a sample X
(random variable); here numbers of events of a type.
(ii). Test statistic or distance statistic: d(X)—the larger its
value the more inconsistent the data are with Η0 in the direction
of alternatives or discrepancies of interest.
d(X): how many excess events of a given type are observed
(from trillions of collisions) in comparison to what would be
expected from background alone (in the form of bumps).
d(X) has a known probability distribution under Η0 (and under
various alternatives).
7. 11/5
(iii). The P-value (or significance level) associated with d(x0)
is the probability of a difference as large or larger than d(x0),
under the assumption that H0 is true:
7
P-value=Pr(d(X) > d(x0); H0)
If the P-value is sufficiently small (e.g., .05, .01, .001)
d(x0) is said to be statistically significant (or significant at the
level reached)
d(X) can be given in terms of standard deviation units, or
sigma units
8. 11/5
8
The distribution of statistic d(X) is the sampling distribution
Pr(d(X) > 1; H0) = .16
Pr(d(X) > 2; H0) = .02
Pr(d(X) > 3; H0) =.001
Pr(d(X) > 4; H0) = .00003
Pr(d(X) > 5; H0)= .0000003
The probability of observing results as or more extreme as 5
sigmas, under H0, is approximately 1 in 3,500,000.
10. 11/5
The actual computations are based on simulating what it would
be like were Η0: μ = 0 (signal strength = 0), fortified with much
cross-checking of results.
So the significance test has:
1) Data x0 and hypotheses Η0: μ = 0 vs. Η1: μ > 0
2) A (distance) test statistic d(X)
3) Probability distribution of d(X) under the null and various
10
alternatives
11. 11/5
11
There’s generally a rule of interpretation:
• if d(X) > 5 sigma, infer discovery
• if d(X) > 2 sigma, get more data
We want methods with high capability to detect discrepancies
while avoiding mistaking spurious bumps as real.
12. 11/5
12
• First stage: test for a real effect
(Cox’s taxonomy: searching for structure)
Not a point against point test!
Cousins: H0 is Standard Model (SM) missing a piece
• Second stage: determine its properties, test SM vs “Beyond
SM” (BSM)
(Cox: embedded)
13. 11/5
13
(IV) The P-Value Police
When the July 2012 report came out, a number of people set
out to grade the different interpretations of the P-value report:
Larry Wasserman (“Normal Deviate” on his blog) called them
the “P-Value Police”.
• Job: to examine if reports by journalists and scientists could
by any stretch of the imagination be seen to have
misinterpreted the sigma levels as posterior probability
assignments to the various models and claims.
David Spiegelhalter: A well-known (Bayesian) statistician: risk
communication.
14. 11/5
14
Thumbs up or down
Thumbs up, to the ATLAS group report:
“A statistical combination of these channels and
others puts the significance of the signal at 5
sigma, meaning that only one experiment in
three million would see an apparent signal this
strong in a universe without a Higgs.”
Thumbs down to reports such as:
“There is less than a one in 3.5 million chance that their
results are a statistical fluke.”
Critics (Spiegelhalter) allege they are misinterpreting the P-value
as a posterior probability on H0.
15. 11/5
15
Not so.
H0 does not say the observed results are due to background
alone, or are flukes,
Η0: μ = 0
Although if H0 were true it follows that various results would
occur with specified probabilities.
(In particular, it entails that large bumps are improbable.)
16. 11/5
In fact it is an ordinary error probability.
Since it’s not just a single result, but a dynamic test procedure,
we can write it:
16
(1) Pr(Test T produces d(X) > 5; H0) ≤ .0000003
Note: (1) is not a conditional probability (that involves a prior)
Pr(Test T produces d(X) > 5 and H0)/ Pr(H0)
17. 11/5
17
(V) Detaching inference(s) from the evidence
True, the inference actually detached goes beyond a P-value
report. Infer:
(2)There is strong evidence for
(first) a genuine discrepancy from H0
(later) H*: a Higgs (or a Higgs-like) particle.
Gradations: indication, evidence, discovery (up to July 4, 2012)
Inferring (2) relies on an implicit principle of evidence.
18. 11/5
Test Principle #1: (statistical significance) Data provide
evidence for a genuine discrepancy from H0 (just) to the
extent that H0 would (very probably) have survived, were
H0 a reasonably adequate description of the process
generating the data.
(1)’ Pr(Test T produces d(X) < 5; H0) > .9999997
• With probability .9999997, the bumps would be smaller,
would behave like flukes, disappear with more data, not be
produced at both CMS and ATLAS, in a world given by H0.
• They didn’t disappear, they grew
(2) So, H*: a Higgs (or a Higgs-like) particle.
18
19. 11/5
19
Following the rule: Interpret 5 sigma bumps as a real effect (a
discrepancy from 0), you’d erroneously interpret data with
probability less than .0000003
An error probability
The warrant isn’t low long-run error (in a case like this) but
detaching an inference based on “strong argument from
coincidence”.
Qualifying claims by how well they have been probed
(precision, accuracy).
20. 11/5
Second Stage
Once the null is rejected, the job shifts to testing if various
parameters agree with the SM predictions.
Now the corresponding null hypothesis is the SM Higgs boson
The null hypothesis at the second stage
20
H[2]
0: SM Higgs boson: μ = 1
and discrepancies from it are probed, estimated with
confidence intervals
(Cousins)
21. 11/5
21
Takes us to the most important role served by statistical
significance tests: (requiring a 5 sigma excess for discovery):
It affords a standard for:
• (a) denying sufficient evidence of a new particle, inferring
“not a genuine effect”, and
• (b) ruling out values of various parameters, e.g., mass
ranges.
22. 11/5
22
(VI) Positive and Negative test results of the analysis
Positive (very low P-value): infer genuine effects
Negative (moderate P-value): deny real effects (infer flukes),
Deny excesses indicate BSM.
• At
both
stages,
they
were
engaged
in
exploration
for
BSM
physics
(beyond
the
standard
model)
• It
combined
testing,
estimating,
exploring.
23. 11/5
23
NYT: “Chasing the Higgs” [Dennis Overbye interviews
spokespeople Gianotti (ATLAS) and Tonelli (CMS).]
• Once a month they got bumps that were random flukes
“So ‘we crosscheck everything’ and ‘try to kill’ any
anomaly that might be merely random.”
They were convinced they had found evidence of extra
dimensions of space time “and then the signal faded like an
old tied balloon.”
24. 11/5
24
• “We’ve made many discoveries,” Dr. Tonelli said,
“most of them false.”
• “Ninety-nine percent of the time, that is just
what happens.”
What’s the difference between HEP physics and social
psychology (and other big data screening) where “most
results in most fields are false”, or so we keep hearing?
HEP physicists don’t publish on the basis of a single “nominal”
(or “local”) P-value.
25. 11/5
25
Look Elsewhere Effect (LEE)
A nominal (or local) P-value: the P-value at a particular, data-determined,
mass.
But the probability of so impressive a difference anywhere in a
mass range would be greater than the local one.
I take it that requiring a smaller P-value (i.e., bigger
difference), at least 5 sigma, is akin to adjusting for multiple
trials or look elsewhere effect LEE.
26. 11/5
26
“Game of Bump-Hunting” (Overbye)
“One bump on physicists’ charts…was disappearing. But
another was blooming like the shy girl at a dance. …. nobody
could remember exactly when she had come in. But she was
the one who would marry the prince.”
“It continued to grow over the fall until it had reached the 3-
sigma level — the chances of being a fluke [spurious
significance] were less than 1 in 740, enough for physicists to
admit it to the realm of “evidence” of something, but not yet a
discovery.”
27. 11/5
Background knowledge of how flukes behave:
• “If they were flukes, more data would make them fade into
the statistical background,
• If not, the bumps would grow in slow motion into a bona
fide discovery.”
• They give the bump a hard time, look at multiple decay
channels, and don’t tell the details of where they found her
to the other team.
• When two independent experiments find the same particle
signal at the same mass, it overcomes the multiple testing
and gives a strong argument.
27
28. 11/5
28
(VII) Possible Anomalies for SM
They also follow up bumps indicating discrepancies with
H[2]
0 SM Higgs boson: μ = 1
Hints of anomalies with the “plain vanilla” particle of the
Standard Model
(viewed as tests or corresponding interval estimates)
Even a year later they examined these anomalies with more
data.
29. 11/5
29
Curb your enthusiasm
Matt Strassler: “The excess (in favor of BSM properties)
became a bit smaller each time…. That’s an unfortunate sign,
if one is hoping the excess isn’t just a statistical fluke.”
Or they’d see the bump at ATLAS… and not CMS
“Taking all of the data, and not cherry picking…there’s
nothing here that you can call “evidence” for the much sought
BSM.” (Strassler)
Considering the frequent flukes, and the hot competition
between the ATLAS and CMS to be first, a tool for when to
“curb their enthusiasm” seems exactly what was wanted.
30. 11/5
So, this “negative” portion involves:
(a) denying BSM anomalies are real
(b) setting upper bounds for these discrepancies with the SM
30
Higgs
Each with its own test statistic and evidence g(x0)
H[2]
0 : SM Higgs boson: μ = 1
Failing to reject the null isn’t evidence for it, but they could set
upper bounds.
31. 11/5
31
Test Principle #2 (for non-significance): Data provide
evidence to rule out a discrepancy δ∗ to the extent that a
larger g(x0) would very probably have resulted if δ were as
great as δ∗
Detach δ < δ∗
(could equivalently be viewed as inferring a confidence
interval estimate δ = g(x0) + ε)
So these tools seem just the thing for this research
32. 11/5
32
(VIII) Conclusion O’Hagan published a digest of responses a
few days later
• “They surely would be willing to announce SM Higgs
discovery if they were 99.99% certain of the existence of
the SM Higgs” (and avoid the ad hoc 5 sigma)
Pr(SM Higgs) = .9999
• It would require prior probabilities to “SM Higgs” claim,
and prior distribution on the numerous “nuisance”
parameters of the background and the signal.
• Multivariate priors, correlations between parameters, joint
priors, and the catchall: P(data|not H*)
33. 11/5
33
• Even if all that were done and agreed upon, it would not
have given the kind of tools needed to find things out
Worse: spiked priors Pr(No SM Higgs)= Pr(SM Higgs)=.5
(not uninformative)
• Physicists believed in SM Higgs before building the big
collider, given the perfect predictive success of SM, its
simplicity–very different than having evidence for a
discovery.
• Others may believe (and fervently wish) that it will break
down somewhere.
34. 11/5
34
P-value police: Those who think we want a posterior
probability in H* might be sliding from what may be inferred
from this legitimate high probability:
Pr(test T would not reach 5 sigma; H0) > .9999997
With probability .9999997, our methods would show
that the bumps disappear, under the assumption data
are due to background H0.
They don’t disappear but grow.
Infer H*
Qualified by the test properties
35. 11/5
35
What’s passed with high severity?
H*: a Higgs boson consistent with the SM (at the levels
of precision and accuracy of these experiments)
An adequate account should also always report alternatives
that have not been well ruled out
• measurements not precise enough to rule out discrepancies
from a SM Higgs as large as 10%, 20%, 50%.
• There are rivals to the SM that would not have been
distinguishable with the given data (which went through a
lot of filtering, and triggering rules).
They will get more data in 2015, there’s talk of a more
precise detector being built
36. 11/5
36
REFERENCES (Online links)
• Atlas
report:
http://cds.cern.ch/record/1494183/files/ATLAS-‐
CONF-‐2012-‐162.pdf
• Atlas
Higgs
experiment,
public
results:
https://twiki.cern.ch/twiki/bin/view/AtlasPublic/HiggsPublicRes
ults
• CMS
Higgs
experiment,
public
results:
https://twiki.cern.ch/twiki/bin/view/CMSPublic/PhysicsResultsH
IG
• Mayo,
D.
G.
and
Cox,
D.
R.
(2010).
"Frequentist
Statistics
as
a
Theory
of
Inductive
Inference"
in
Error
and
Inference:
Recent
Exchanges
on
Experimental
Reasoning,
Reliability
and
the
Objectivity
and
Rationality
of
Science
(D
Mayo
and
A.
Spanos
eds.),
Cambridge:
Cambridge
University
Press:
1-‐27.
This
paper
appeared
in
The
Second
Erich
L.
Lehmann
Symposium:
Optimality,
2006,
Lecture
37. 11/5
37
Notes-‐Monograph
Series,
Volume
49,
Institute
of
Mathematical
Statistics,
pp.
247-‐275.
• Cousins,
R.
(2014).
“The Jeffreys-Lindley Paradox and Discovery
Criteria in High Energy Physics” http://arxiv.org/abs/1310.3791
• O’Hagan
letter:
§ Original
letter
with
responses:
http://bayesian.org/forums/news/3648
§ 1st
link
in
a
group
of
discussions
of
the
letter:
http://errorstatistics.com/2012/07/11/is-‐particle-‐
physics-‐bad-‐science/
• Overbye,
D.
(March
15,
2013)
“Chasing
the
Higgs,”
New
York
Times:
http://www.nytimes.com/2013/03/05/science/chasing-‐the-‐
higgs-‐boson-‐how-‐2-‐teams-‐of-‐rivals-‐at-‐CERN-‐searched-‐for-‐physics-‐
most-‐elusive-‐particle.html?pagewanted=all&_r=0
38. 11/5
38
• Spiegelhalter,
D.
(August
7,
2012)
blog,
Understanding
Uncertainty
,
“Explaining
5
sigma
for
the
Higgs:
how
well
did
they
do?”
http://understandinguncertainty.org/explaining-‐5-‐sigma-‐higgs-‐
how-‐well-‐did-‐they-‐do
• Strassler,
M.
(July
2,
2013)
blog,
Of
Particular
Significance,
“A
Second
Higgs
Particle”:
http://profmattstrassler.com/2013/07/02/a-‐second-‐higgs-‐
particle/
• Wasserman,
L.
(July
11,
2012)
blog,
Normal
Deviate,
“The
Higgs
Boson
and
the
P-‐Value
Police”:
http://normaldeviate.wordpress.com/2012/07/11/the-‐higgs-‐
boson-‐and-‐the-‐p-‐value-‐police/