This was initially posted as slides from our joint Spring 2014 seminar: “Talking Back to the Critics Using Error Statistics”. (You can enlarge them.) Related reading is Mayo and Spanos (2011)
This was initially posted as slides from our joint Spring 2014 seminar: “Talking Back to the Critics Using Error Statistics”. (You can enlarge them.) Related reading is Mayo and Spanos (2011)
Below are the slides from my Rutgers seminar for the Department of Statistics and Biostatistics yesterday, since some people have been asking me for them. The abstract is here. I don’t know how explanatory a bare outline like this can be, but I’d be glad to try and answer questions[i]. I am impressed at how interested in foundational matters I found the statisticians (both faculty and students) to be. (There were even a few philosophers in attendance.) It was especially interesting to explore, prior to the seminar, possible connections between severity assessments and confidence distributions, where the latter are along the lines of Min-ge Xie (some recent papers of his may be found here.)
“Probing with Severity: Beyond Bayesian Probabilism and Frequentist Performance”
[i]They had requested a general overview of some issues in philosophical foundations of statistics. Much of this will be familiar to readers of this blog.
The NY Times Magazine had a feature on the Amazing Randi yesterday, “The Unbelievable Skepticism of the Amazing Randi.” It described one of the contestants in Randi’s most recent Million Dollar Challenge, Fei Wang:
“[Wang] claimed to have a peculiar talent: from his right hand, he could transmit a mysterious force a distance of three feet, unhindered by wood, metal, plastic or cardboard. The energy, he said, could be felt by others as heat, pressure, magnetism or simply “an indescribable change.” Tonight, if he could demonstrate the existence of his ability under scientific test conditions, he stood to win $1 million.”
Isn’t “an indescribable change” rather vague?
…..The Challenge organizers had spent weeks negotiating with Wang and fine-tuning the protocol for the evening’s test. A succession of nine blindfolded subjects would come onstage and place their hands in a cardboard box. From behind a curtain, Wang would transmit his energy into the box. If the subjects could successfully detect Wang’s energy on eight out of nine occasions, the trial would confirm Wang’s psychic power. …”
After two women failed to detect the “mystic force” the M.C. announced the contest was over.
“With two failures in a row, it was impossible for Wang to succeed. The Million Dollar Challenge was already over.”
You think they might have given him another chance or something.
“Stepping out from behind the curtain, Wang stood center stage, wearing an expression of numb shock, like a toddler who has just dropped his ice cream in the sand. He was at a loss to explain what had gone wrong; his tests with a paranormal society in Boston had all succeeded. Nothing could convince him that he didn’t possess supernatural powers. ‘This energy is mysterious,’ he told the audience. ‘It is not God.’ He said he would be back in a year, to try again.”
The article is here. If you don’t know who A. Randi is, you should read it.
Randi, much better known during Uri Geller spoon-bending days, has long been the guru to skeptics and fraudbusters, but also a hero to some critical psi believers like I.J. Good. Geller continually sued Randi for calling him a fraud. As such, I.J. Good warned me that I might be taking a risk in my use of “gellerization” in EGEK (1996), but I guess Geller doesn’t read philosophy of science. A post on “Statistics and ESP Research” and Diaconis is here.
I’d love to have seen Randi break out of these chains!
The biennial meeting of the Philosophy of Science Association (PSA) starts this week (Nov. 6-9) in Chicago, together with the History of Science Society. I’ll be part of the symposium:
Summary
“A 5 sigma effect!” is how the recent Higgs boson discovery was reported. Yet before the dust had settled, the very nature and rationale of the 5 sigma (or 5 standard deviation) discovery criteria began to be challenged and debated both among scientists and in the popular press. Why 5 sigma? How is it to be interpreted? Do p-values in high-energy physics (HEP) avoid controversial uses and misuses of p-values in social and other sciences? The goal of our symposium is to combine the insights of philosophers and scientists whose work interrelates philosophy of statistics, data analysis and modeling in experimental physics, with critical perspectives on how discoveries proceed in practice. Our contributions will link questions about the nature of statistical evidence, inference, and discovery with questions about the very creation of standards for interpreting and communicating statistical experiments. We will bring out some unique aspects of discovery in modern HEP. We also show the illumination the episode offers to some of the thorniest issues revolving around statistical inference, frequentist and Bayesian methods, and the philosophical, technical, social, and historical dimensions of scientific discovery.
Questions:
1) How do philosophical problems of statistical inference interrelate with debates about inference and modeling in high energy physics (HEP)?
2) Have standards for scientific discovery in particle physics shifted? And if so, how has this influenced when a new phenomenon is “found”?
3) Can understanding the roles of statistical hypotheses tests in HEP resolve classic problems about their justification in both physical and social sciences?
4) How do pragmatic, epistemic and non-epistemic values and risks influence the collection, modeling, and interpretation of data in HEP?
Abstracts for Individual Presentations
(1) Unresolved Philosophical Issues Regarding Hypothesis Testing in High Energy Physics
Robert D. Cousins.
Professor, Department of Physics and Astronomy, University of California, Los Angeles (UCLA)
The discovery and characterization of a Higgs boson in 2012-2013 provide multiple examples of statistical inference as practiced in high energy physics (elementary particle physics). The main methods employed have a decidedly frequentist flavor, drawing in a pragmatic way on both Fisher’s ideas and the Neyman-Pearson approach. A physics model being tested typically has a “law of nature” at its core, with parameters of interest representing masses, interaction strengths, and other presumed “constants of nature”. Additional “nuisance parameters” are needed to characterize the complicated measurement processes. The construction of confidence intervals for a parameter of interest q is dual to hypothesis testing, in that the test of the null hypothesis q=q_{0} at significance level (“size”) a is equivalent to whether q_{0} is contained in a confidence interval for q with confidence level (CL) equal to 1-a. With CL or a specified in advance (“pre-data”), frequentist coverage properties can be assured, at least approximately, although nuisance parameters bring in significant complications. With data in hand, the post-data p-value can be defined as the smallest significance level a at which the null hypothesis would be rejected, had that a been specified in advance. Carefully calculated p-values (not assuming normality) are mapped onto the equivalent number of standard deviations (“s”) in a one-tailed test of the mean of a normal distribution. For a discovery such as the Higgs boson, experimenters report both p-values and confidence intervals of interest. Continue reading
From Gelman’s blog:
“In one of life’s horrible ironies, I wrote a paper “Why we (usually) don’t have to worry about multiple comparisons” but now I spend lots of time worrying about multiple comparisons”
Posted by 14 October 2014, 11:13 am on
Exhibit B: The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time, in press. (Andrew Gelman and Eric Loken) (Shortened version is here.)
The “forking paths” paper, in my reading, basically argues that mere hypothetical possibilities about what you would or might have done had the data been different (in order to secure a desired interpretation) suffices to alter the characteristics of the analysis you actually did. That’s an error statistical argument–maybe even stronger than what some error statisticians would say. What’s really being condemned are overly flexible ways to move from statistical results to substantive claims. The p-values are illicit when taken to provide evidence for those claims because an actual p-value requires Prob(P < p;Ho) = p (and the actual p-value has become much greater by design). The criticism makes perfect sense if you’re scrutinizing inferences according to how well or severely tested they are. Actual error probabilities are accordingly altered or unable to be calculated. However, if one is going to scrutinize inferences according to severity then the same problematic flexibility would apply to Bayesian analyses, whether or not they have a way to pick up on it. (It’s problematic if they don’t.) I don’t see the magic by which a concern for multiple testing disappears in Bayesian analysis (e.g., in the first paper) except by assuming some prior takes care of it.
See my comment here.
Memory Lane* in Honor of C.S. Peirce’s Birthday:
(Part 3) of “Peircean Induction and the Error-Correcting Thesis”
Deborah G. Mayo
Transactions of the Charles S. Peirce Society 41(2) 2005: 299-319
(9/10) Peircean Induction and the Error-Correcting Thesis (Part I)
(9/10) (Part 2) Peircean Induction and the Error-Correcting Thesis
8. Random sampling and the uniformity of nature
We are now at the point to address the final move in warranting Peirce’s [self-correcting thesis] SCT. The severity or trustworthiness assessment, on which the error correcting capacity depends, requires an appropriate link (qualitative or quantitative) between the data and the data generating phenomenon, e.g., a reliable calibration of a scale in a qualitative case, or a probabilistic connection between the data and the population in a quantitative case. Establishing such a link, however, is regarded as assuming observed regularities will persist, or making some “uniformity of nature” assumption—the bugbear of attempts to justify induction.
But Peirce contrasts his position with those favored by followers of Mill, and “almost all logicians” of his day, who “commonly teach that the inductive conclusion approximates to the truth because of the uniformity of nature” (2.775). Inductive inference, as Peirce conceives it (i.e., severe testing) does not use the uniformity of nature as a premise. Rather, the justification is sought in the manner of obtaining data. Justifying induction is a matter of showing that there exist methods with good error probabilities. For this it suffices that randomness be met only approximately, that inductive methods check their own assumptions, and that they can often detect and correct departures from randomness.
… It has been objected that the sampling cannot be random in this sense. But this is an idea which flies far away from the plain facts. Thirty throws of a die constitute an approximately random sample of all the throws of that die; and that the randomness should be approximate is all that is required. (1.94)
Peirce backs up his defense with robustness arguments. For example, in an (attempted) Binomial induction, Peirce asks, “what will be the effect upon inductive inference of an imperfection in the strictly random character of the sampling” (2.728). What if, for example, a certain proportion of the population had twice the probability of being selected? He shows that “an imperfection of that kind in the random character of the sampling will only weaken the inductive conclusion, and render the concluded ratio less determinate, but will not necessarily destroy the force of the argument completely” (2.728). This is particularly so if the sample mean is near 0 or 1. In other words, violating experimental assumptions may be shown to weaken the trustworthiness or severity of the proceeding, but this may only mean we learn a little less.
Yet a further safeguard is at hand:
Nor must we lose sight of the constant tendency of the inductive process to correct itself. This is of its essence. This is the marvel of it. …even though doubts may be entertained whether one selection of instances is a random one, yet a different selection, made by a different method, will be likely to vary from the normal in a different way, and if the ratios derived from such different selections are nearly equal, they may be presumed to be near the truth. (2.729)
Here, the marvel is an inductive method’s ability to correct the attempt at random sampling. Still, Peirce cautions, we should not depend so much on the self-correcting virtue that we relax our efforts to get a random and independent sample. But if our effort is not successful, and neither is our method robust, we will probably discover it. “This consideration makes it extremely advantageous in all ampliative reasoning to fortify one method of investigation by another” (ibid.). Continue reading
I’m reblogging a few of the Higgs posts, with some updated remarks, on this two-year anniversary of the discovery. (The first was in my last post.) The following, was originally “Higgs Analysis and Statistical Flukes: part 2″ (from March, 2013).[1]
Some people say to me: “This kind of reasoning is fine for a ‘sexy science’ like high energy physics (HEP)”–as if their statistical inferences are radically different. But I maintain that this is the mode by which data are used in “uncertain” reasoning across the entire landscape of science and day-to-day learning (at least, when we’re trying to find things out)[2] Even with high level theories, the particular problems of learning from data are tackled piecemeal, in local inferences that afford error control. Granted, this statistical philosophy differs importantly from those that view the task as assigning comparative (or absolute) degrees-of-support/belief/plausibility to propositions, models, or theories. Continue reading
Four score years ago (!) we held the conference “Statistical Science and Philosophy of Science: Where Do (Should) They meet?” at the London School of Economics, Center for the Philosophy of Natural and Social Science, CPNSS, where I’m visiting professor [1] Many of the discussions on this blog grew out of contributions from the conference, and conversations initiated soon after. The conference site is here; my paper on the general question is here.[2]
My main contribution was “Statistical Science Meets Philosophy of Science Part 2: Shallow versus Deep Explorations” SS & POS 2. It begins like this:
1. Comedy Hour at the Bayesian Retreat[3]
Overheard at the comedy hour at the Bayesian retreat: Did you hear the one about the frequentist… Continue reading
Aris Spanos
Wilson E. Schmidt Professor of Economics
Department of Economics, Virginia Tech
Recurring controversies about P values and conﬁdence intervals revisited*
Ecological Society of America (ESA) ECOLOGY
Forum—P Values and Model Selection (pp. 609-654)
Volume 95, Issue 3 (March 2014): pp. 645-651
INTRODUCTION
The use, abuse, interpretations and reinterpretations of the notion of a P value has been a hot topic of controversy since the 1950s in statistics and several applied ﬁelds, including psychology, sociology, ecology, medicine, and economics.
The initial controversy between Fisher’s signiﬁcance testing and the Neyman and Pearson (N-P; 1933) hypothesis testing concerned the extent to which the pre-data Type I error probability α can address the arbitrariness and potential abuse of Fisher’s post-data threshold for the P value. Continue reading
If there’s somethin’ strange in your neighborhood. Who ya gonna call?(Fisherian Fraudbusters!)*
*[adapted from R. Parker’s “Ghostbusters”]
When you need to warrant serious accusations of bad statistics, if not fraud, where do scientists turn? Answer: To the frequentist error statistical reasoning and to p-value scrutiny, first articulated by R.A. Fisher[i].The latest accusations of big time fraud in social psychology concern the case of Jens Förster. As Richard Gill notes:
Aris Spanos’ overview of error statistical responses to familiar criticisms of statistical tests. Related reading is Mayo and Spanos (2011)
We interspersed key issues from the reading for this session (from Howson and Urbach) with portions of my presentation at the Boston Colloquium (Feb, 2014): Revisiting the Foundations of Statistics in the Era of Big Data: Scaling Up to Meet the Challenge. (Slides below)*.
Someone sent us a recording (mp3)of the panel discussion from that Colloquium (there’s a lot on “big data” and its politics) including: Mayo, Xiao-Li Meng (Harvard), Kent Staley (St. Louis), and Mark van der Laan (Berkeley).
See if this works: | mp3
*There’s a prelude here to our visitor on April 24: Professor Stanley Young from the National Institute of Statistical Sciences.
We spent the first half of Thursday’s seminar discussing the Fisher, Neyman, and E. Pearson “triad”[i]. So, since it’s Saturday night, join me in rereading for the nth time these three very short articles. The key issues were: error of the second kind, behavioristic vs evidential interpretations, and Fisher’s mysterious fiducial intervals. Although we often hear exaggerated accounts of the differences in the Fisherian vs Neyman-Pearson (NP) methodology, in fact, N-P were simply providing Fisher’s tests with a logical ground (even though other foundations for tests are still possible), and Fisher welcomed this gladly. Notably, with the single null hypothesis, N-P showed that it was possible to have tests where the probability of rejecting the null when true exceeded the probability of rejecting it when false. Hacking called such tests “worse than useless”, and N-P develop a theory of testing that avoids such problems. Statistical journalists who report on the alleged “inconsistent hybrid” (a term popularized by Gigerenzer) should recognize the extent to which the apparent disagreements on method reflect professional squabbling between Fisher and Neyman after 1935 [A recent example is a Nature article by R. Nuzzo in ii below]. The two types of tests are best seen as asking different questions in different contexts. They both follow error-statistical reasoning. Continue reading
News Flash! Congratulations to Cosma Shalizi who announced yesterday that he’d been granted tenure (Statistics, Carnegie Mellon). Cosma is a leading error statistician, a creative polymath and long-time blogger (at Three-Toad sloth). Shalizi wrote an early book review of EGEK (Mayo 1996)* that people still send me from time to time, in case I hadn’t seen it! You can find it on this blog from 2 years ago (posted by Jean Miller). A discussion of a meeting of the minds between Shalizi and Andrew Gelman is here.
*Error and the Growth of Experimental Knowledge.
Those reading Popper[i] with us might be interested in an (undergraduate) item I came across: Popper Self-Test Questions. It includes multiple choice questions, quotes to ponder, and thumbnail definitions at the end[ii].
[i]Popper reading (for Feb 13, 2014) from Conjectures and Refutations
[ii]I might note the “No-Pain philosophy” (3 part) Popper posts from this blog: parts 1, 2, and 3.
FURTHER UPDATED: New course for Spring 2014: Thurs 3:30-6:15 (Randolph 209)
first installment 6334 syllabus_SYLLABUS (first) Phil 6334: Philosophy of Statistical Inference and Modeling
D. Mayo and A. Spanos
Contact: error@vt.edu
This new course, to be jointly taught by Professors D. Mayo (Philosophy) and A. Spanos (Economics) will provide an introductory, in-depth introduction to graduate level research in philosophy of inductive-statistical inference and probabilistic methods of evidence (a branch of formal epistemology). We explore philosophical problems of confirmation and induction, the philosophy and history of frequentist and Bayesian approaches, and key foundational controversies surrounding tools of statistical data analytics, modeling and hypothesis testing in the natural and social sciences, and in evidence-based policy.
We now have some tentative topics and dates:
1. 1/23 | Introduction to the Course: 4 waves of controversy in the philosophy of statistics |
2. 1/30 | How to tell what’s true about statistical inference: Probabilism, performance and probativeness |
3. 2/6 | Induction and Confirmation: Formal Epistemology |
4. 2/13 | Induction, falsification, severe tests: Popper and Beyond |
5. 2/20 | Statistical models and estimation: the Basics |
6. 2/27 | Fundamentals of significance tests and severe testing |
7. 3/6 | Five sigma and the Higgs Boson discovery Is it “bad science”? |
SPRING BREAK Statistical Exercises While Sunning | |
8. 3/20 | Fraudbusting and Scapegoating: Replicability and big data: are most scientific results false? |
9. 3/27 | How can we test the assumptions of statistical models? All models are false; no methods are objective: Philosophical problems of misspecification testing: Spanos method |
10. 4/3 | Fundamentals of Statistical Testing: Family Feuds and 70 years of controversy |
11. 4/10 | Error Statistical Philosophy: Highly Probable vs Highly Probed Some howlers of testing |
12. 4/17 | What ever happened to Bayesian Philosophical Foundations? Dutch books etc. Fundamental of Bayesian statistics |
13. 4/24 | Bayesian-frequentist reconciliations, unifications, and O-Bayesians |
14. 5/1 | Overview: Answering the critics: Should statistical philosophy be divorced from methodology? |
(15. TBA) | Topic to be chosen (Resampling statistics and new journal policies? Likelihood principle) |
Interested in attending? E.R.R.O.R.S.* can fund travel (presumably driving) and provide accommodation for Thurs. night in a conference lodge in Blacksburg for a few people through (or part of) the semester. If interested, write ASAP for details (with a brief description of your interest and background) to error@vt.edu. (Several people asked about long-distance hook-ups: We will try to provide some sessions by Skype, and will put each of the seminar items here (also check the Phil6334 page on this blog).
A sample of questions we consider*:
D. Mayo (books):
How to Tell What’s True About Statistical Inference, (Cambridge, in progress).
Error and the Growth of Experimental Knowledge, Chicago: Chicago University Press, 1996. (Winner of 1998 Lakatos Prize).
Acceptable Evidence: Science and Values in Risk Management, co-edited with Rachelle Hollander, New York: Oxford University Press, 1994.
Aris Spanos (books):
Probability Theory and Statistical Inference, Cambridge, 1999.
Statistical Foundations of Econometric Modeling, Cambridge, 1986.
Joint (books): Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability and the Objectivity and Rationality of Science, D. Mayo & A. Spanos (eds.), Cambridge: Cambridge University Press, 2010. [The book includes both papers and exchanges between Mayo and A. Chalmers, A. Musgrave, P. Achinstein, J. Worrall, C. Glymour, A. Spanos, and joint papers with Mayo and Sir David Cox].
Andrew Gelman says that as a philosopher, I should appreciate his blog today in which he records his frustration: “Against aggressive definitions: No, I don’t think it helps to describe Bayes as ‘the analysis of subjective beliefs’…” Gelman writes:
I get frustrated with what might be called “aggressive definitions,” where people use a restrictive definition of something they don’t like. For example, Larry Wasserman writes (as reported by Deborah Mayo):
“I wish people were clearer about what Bayes is/is not and what frequentist inference is/is not. Bayes is the analysis of subjective beliefs but provides no frequency guarantees. Frequentist inference is about making procedures that have frequency guarantees but makes no pretense of representing anyone’s beliefs.”
I’ll accept Larry’s definition of frequentist inference. But as for his definition of Bayesian inference: No no no no no. The probabilities we use in our Bayesian inference are not subjective, or, they’re no more subjective than the logistic regressions and normal distributions and Poisson distributions and so forth that fill up all the textbooks on frequentist inference.
To quickly record some of my own frustrations:*: First, I would disagree with Wasserman’s characterization of frequentist inference, but as is clear from Larry’s comments to (my reaction to him), I think he concurs that he was just giving a broad contrast. Please see Note [1] for a remark from my post: Comments on Wasserman’s “what is Bayesian/frequentist inference?” Also relevant is a Gelman post on the Bayesian name: [2].
Second, Gelman’s “no more subjective than…” evokes remarks I’ve made before. For example, in “What should philosophers of science do…” I wrote:
Arguments given for some very popular slogans (mostly by non-philosophers), are too readily taken on faith as canon by others, and are repeated as gospel. Examples are easily found: all models are false, no models are falsifiable, everything is subjective, or equally subjective and objective, and the only properly epistemological use of probability is to supply posterior probabilities for quantifying actual or rational degrees of belief. Then there is the cluster of “howlers” allegedly committed by frequentist error statistical methods repeated verbatim (discussed on this blog).
I’ve written a lot about objectivity on this blog, e.g., here, here and here (and in real life), but what’s the point if people just rehearse the “everything is a mixture…” line, without making deeply important distinctions? I really think that, next to the “all models are false” slogan, the most confusion has been engendered by the “no methods are objective” slogan. However much we may aim at objective constraints, it is often urged, we can never have “clean hands” free of the influence of beliefs and interests, and we invariably sully methods of inquiry by the entry of background beliefs and personal judgments in their specification and interpretation. Continue reading
The blog “It’s Chancy” (Corey Yanofsky) has a post today about “two severities” which warrants clarification. Two distinctions are being blurred: between formal and informal severity assessments, and between a statistical philosophy (something Corey says he’s interested in) and its relevance to philosophy of science (which he isn’t). I call the latter an error statistical philosophy of science. The former requires both formal, semi-formal and informal severity assessments. Here’s his post:
In the comments to my first post on severity, Professor Mayo noted some apparent and some actual misstatements of her views.To avert misunderstandings, she directed readers to two of her articles, one of which opens by making this distinction:
“Error statistics refers to a standpoint regarding both (1) a general philosophy of science and the roles probability plays in inductive inference, and (2) a cluster of statistical tools, their interpretation, and their justiﬁcation.”
In Mayo’s writings I see two interrelated notions of severity corresponding to the two items listed in the quote: (1) an informal severity notion that Mayo uses when discussing philosophy of science and specific scientific investigations, and (2) Mayo’s formalization of severity at the data analysis level.
One of my besetting flaws is a tendency to take a narrow conceptual focus to the detriment of the wider context. In the case of Severity, part one, I think I ended up making claims about severity that were wrong. I was narrowly focused on severity in sense (2) — in fact, on one specific equation within (2) — but used a mish-mash of ideas and terminology drawn from all of my readings of Mayo’s work. When read through a philosophy-of-science lens, the result is a distorted and misstated version of severity in sense (1) .
As a philosopher of science, I’m a rank amateur; I’m not equipped to add anything to the conversation about severity as a philosophy of science. My topic is statistics, not philosophy, and so I want to warn readers against interpreting Severity, part one as a description of Mayo’s philosophy of science; it’s more of a wordy introduction to the formal definition of severity in sense (2).[It’s Chancy, Jan 11, 2014)
A needed clarification may be found in a post of mine which begins:
Error statistics: (1) There is a “statistical philosophy” and a philosophy of science. (a) An error-statistical philosophy alludes to the methodological principles and foundations associated with frequentist error-statistical methods. (b) An error-statistical philosophy of science, on the other hand, involves using the error-statistical methods, formally or informally, to deal with problems of philosophy of science: to model scientific inference (actual or rational), to scrutinize principles of inference, and to address philosophical problems about evidence and inference (the problem of induction, underdetermination, warranting evidence, theory testing, etc.).
I assume the interest here* is on the former, (a). I have stated it in numerous ways, but the basic position is that inductive inference—i.e., data-transcending inference—calls for methods of controlling and evaluating error probabilities (even if only approximate). An inductive inference, in this conception, takes the form of inferring hypotheses or claims to the extent that they have been well tested. It also requires reporting claims that have not passed severely, or have passed with low severity. In the “severe testing” philosophy of induction, the quantitative assessment offered by error probabilities tells us not “how probable” but, rather, “how well probed” hypotheses are. The local canonical hypotheses of formal tests and estimation methods need not be the ones we entertain post data; but they give us a place to start without having to go “the designer-clothes” route.
The post-data interpretations might be formal, semi-formal, or informal.
See also: Staley’s review of Error and Inference (Mayo and Spanos eds.)
New course for Spring 2014: Thursday 3:30-6:15
Phil 6334: Philosophy of Statistical Inference and Modeling
D. Mayo and A. Spanos
Contact: error@vt.edu
This new course, to be jointly taught by Professors D. Mayo (Philosophy) and A. Spanos (Economics) will provide an introductory, in-depth introduction to graduate level research in philosophy of inductive-statistical inference and probabilistic methods of evidence (a branch of formal epistemology). We explore philosophical problems of confirmation and induction, the philosophy and history of frequentist and Bayesian approaches, and key foundational controversies surrounding tools of statistical data analytics, modeling and hypothesis testing in the natural and social sciences, and in evidence-based policy.
A sample of questions we consider*:
Interested in attending? E.R.R.O.R.S.* can fund travel (presumably driving) and provide lodging for Thurs. night in a conference lodge in Blacksburg for a few people through (or part of) the semester. Topics will be posted over the next week, but if you might be interested, write ASAP for details (with a brief description of your interest and background) to error@vt.edu.
*This course will be a brand new version of related seminar we’ve led in the past, so we don’t have the syllabus set yet. We’re going to try something different this time. I’ll be updating in subsequent installments to the blog.
Dates: January 23, 30; February 6, 13, 20, 27; March 6, [March 8-16 break], 20, 27; April 3,10, 17, 24; May 1
D. Mayo (books):
How to Tell What’s True About Statistical Inference, (Cambridge, in progress).
Error and the Growth of Experimental Knowledge, Chicago: Chicago University Press, 1996. (Winner of 1998 Lakatos Prize).
Acceptable Evidence: Science and Values in Risk Management, co-edited with Rachelle Hollander, New York: Oxford University Press, 1994.
Aris Spanos (books):
Probability Theory and Statistical Inference, Cambridge, 1999.
Statistical Foundations of Econometric Modeling, Cambridge, 1986.
Joint (books): Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability and the Objectivity and Rationality of Science, D. Mayo & A. Spanos (eds.), Cambridge: Cambridge University Press, 2010. [The book includes both papers and exchanges between Mayo and A. Chalmers, A. Musgrave, P. Achinstein, J. Worrall, C. Glymour, A. Spanos, and joint papers with Mayo and Sir David Cox].