Philosophy of Statistics

Allan Birnbaum: Foundations of Probability and Statistics (27 May 1923 – 1 July 1976)

Posted on May 27, 2017 by Mayo
27 May 1923-1 July 1976

27 May 1923-1 July 1976

Today is Allan Birnbaum’s birthday. In honor of his birthday, I’m posting the articles in the Synthese volume that was dedicated to his memory in 1977. The editors describe it as their way of  “paying homage to Professor Birnbaum’s penetrating and stimulating work on the foundations of statistics”. I paste a few snippets from the articles by Giere and Birnbaum. If you’re interested in statistical foundations, and are unfamiliar with Birnbaum, here’s a chance to catch up. (Even if you are, you may be unaware of some of these key papers.)

HAPPY BIRTHDAY ALLAN!

Synthese Volume 36, No. 1 Sept 1977: Foundations of Probability and Statistics, Part I

Editorial Introduction:

This special issue of Synthese on the foundations of probability and statistics is dedicated to the memory of Professor Allan Birnbaum. Professor Birnbaum’s essay ‘The Neyman-Pearson Theory as Decision Theory; and as Inference Theory; with a Criticism of the Lindley-Savage Argument for Bayesian Theory’ was received by the editors of Synthese in October, 1975, and a decision was made to publish a special symposium consisting of this paper together with several invited comments and related papers. The sad news about Professor Birnbaum’s death reached us in the summer of 1976, but the editorial project could nevertheless be completed according to the original plan. By publishing this special issue we wish to pay homage to Professor Birnbaum’s penetrating and stimulating work on the foundations of statistics. We are grateful to Professor Ronald Giere who wrote an introductory essay on Professor Birnbaum’s concept of statistical evidence and who compiled a list of Professor Birnbaum’s publications.

THE EDITORS

Continue reading →

Categories: Birnbaum, Likelihood Principle, Statistics, strong likelihood principle | Tags: | 1 Comment

Slides from the Boston Colloquium for Philosophy of Science: “Severe Testing: The Key to Error Correction”

Posted on March 26, 2017 by Mayo

Slides from my March 17 presentation on “Severe Testing: The Key to Error Correction” given at the Boston Colloquium for Philosophy of Science Alfred I.Taub forum on “Understanding Reproducibility and Error Correction in Science.”

 

Categories: fallacy of rejection, Fisher, fraud, frequentist/Bayesian, Likelihood Principle, reforming the reformers | 16 Comments

BOSTON COLLOQUIUM FOR PHILOSOPHY OF SCIENCE: Understanding Reproducibility & Error Correction in Science

Posted on January 7, 2017 by Mayo

BOSTON COLLOQUIUM FOR PHILOSOPHY OF SCIENCE

2016–2017
57th Annual Program

Download the 57th Annual Program

The Alfred I. Taub forum:

UNDERSTANDING REPRODUCIBILITY & ERROR CORRECTION IN SCIENCE

Cosponsored by GMS and BU’s BEST at Boston University.
Friday, March 17, 2017
1:00 p.m. – 5:00 p.m.
The Terrace Lounge, George Sherman Union
775 Commonwealth Avenue

  • Reputation, Variation, &, Control: Historical Perspectives
    Jutta Schickore History and Philosophy of Science & Medicine, Indiana University, Bloomington.
  • Crisis in Science: Time for Reform?
    Arturo Casadevall Molecular Microbiology & Immunology, Johns Hopkins
  • Severe Testing: The Key to Error Correction
    Deborah Mayo Philosophy, Virginia Tech
  • Replicate That…. Maintaining a Healthy Failure Rate in Science
    Stuart Firestein Biological Sciences, Columbia

 

boston-mayo-2017

Categories: Announcement, philosophy of science, Philosophy of Statistics, Statistical fraudbusting, Statistics | Leave a comment

The Myth of ‘The Myth of Objectivity” (i)

Posted on September 18, 2016 by Mayo
images-28Objectivity in statistics, as in science more generally, is a matter of both aims and methods. Objective science, in our view, aims to find out what is the case as regards aspects of the world [that hold] independently of our beliefs, biases and interests; thus objective methods aim for the critical control of inference and hypotheses, constraining them by evidence and checks of error. (Cox and Mayo 2010, p. 276)


I. The myth of objectivity. Whenever you come up against blanket slogans such as “no methods are objective” or “all methods are equally objective and subjective,” it is a good guess that the problem is being trivialized into oblivion. Yes, there are judgments, disagreements, and values in any human activity, which alone makes it too trivial an observation to distinguish among very different ways that threats of bias and unwarranted inferences may be controlled. Is the objectivity-subjectivity distinction really toothless as many will have you believe? I say no.

Cavalier attitudes toward objectivity are in tension with widely endorsed movements to promote replication, reproducibility, and to come clean on a number of sources behind illicit results: multiple testing, cherry picking, failed assumptions, researcher latitude, publication bias and so on. The moves to take back science–if they are not mere lip-service–are rooted in the supposition that we can more objectively scrutinize results,even if it’s only to point out those that are poorly tested. The fact that the term “objectivity” is used equivocally should not be taken as grounds to oust it, but rather to engage in the difficult work of identifying what there is in “objectivity” that we won’t give up, and shouldn’t. Continue reading →

Categories: Background knowledge | Tags: | 6 Comments

A. Birnbaum: Statistical Methods in Scientific Inference (May 27, 1923 – July 1, 1976)

Posted on July 1, 2016 by Mayo

Allan Birnbaum: May 27, 1923- July 1, 1976

Allan Birnbaum died 40 years ago today. He lived to be only 53 [i]. From the perspective of philosophy of statistics and philosophy of science, Birnbaum is best known for his work on likelihood, the Likelihood Principle [ii], and for his attempts to blend concepts of likelihood with error probability ideas to arrive at what he termed “concepts of statistical evidence”. Failing to find adequate concepts of statistical evidence, Birnbaum called for joining the work of “interested statisticians, scientific workers and philosophers and historians of science”–an idea I have heartily endorsed. While known for a result that the (strong) Likelihood Principle followed from sufficiency and conditionality principles (a result that Jimmy Savage deemed one of the greatest breakthroughs in statistics), a few years after publishing it, he turned away from it, perhaps discovering gaps in his argument. A post linking to a 2014 Statistical Science issue discussing Birnbaum’s result is here. Reference [5] links to the Synthese 1977 volume dedicated to his memory. The editors describe it as their way of “paying homage to Professor Birnbaum’s penetrating and stimulating work on the foundations of statistics”. Ample weekend reading! Continue reading →

Categories: Birnbaum, Likelihood Principle, phil/history of stat, Statistics | Tags: | 62 Comments

Allan Birnbaum: Foundations of Probability and Statistics (27 May 1923 – 1 July 1976)

Posted on May 27, 2016 by Mayo
27 May 1923-1 July 1976

27 May 1923-1 July 1976

Today is Allan Birnbaum’s birthday. In honor of his birthday this year, I’m posting the articles in the Synthese volume that was dedicated to his memory in 1977. The editors describe it as their way of  “paying homage to Professor Birnbaum’s penetrating and stimulating work on the foundations of statistics”. I paste a few snippets from the articles by Giere and Birnbaum. If you’re interested in statistical foundations, and are unfamiliar with Birnbaum, here’s a chance to catch up.(Even if you are,you may be unaware of some of these key papers.)

HAPPY BIRTHDAY ALLAN!

Synthese Volume 36, No. 1 Sept 1977: Foundations of Probability and Statistics, Part I

Editorial Introduction:

This special issue of Synthese on the foundations of probability and statistics is dedicated to the memory of Professor Allan Birnbaum. Professor Birnbaum’s essay ‘The Neyman-Pearson Theory as Decision Theory; and as Inference Theory; with a Criticism of the Lindley-Savage Argument for Bayesian Theory’ was received by the editors of Synthese in October, 1975, and a decision was made to publish a special symposium consisting of this paper together with several invited comments and related papers. The sad news about Professor Birnbaum’s death reached us in the summer of 1976, but the editorial project could nevertheless be completed according to the original plan. By publishing this special issue we wish to pay homage to Professor Birnbaum’s penetrating and stimulating work on the foundations of statistics. We are grateful to Professor Ronald Giere who wrote an introductory essay on Professor Birnbaum’s concept of statistical evidence and who compiled a list of Professor Birnbaum’s publications.

THE EDITORS

Continue reading →

Categories: Birnbaum, Error Statistics, Likelihood Principle, Statistics, strong likelihood principle | 7 Comments

“Intentions” is the new code word for “error probabilities”: Allan Birnbaum’s Birthday

Posted on May 27, 2015 by Mayo
27 May 1923-1 July 1976

27 May 1923-1 July 1976

Today is Allan Birnbaum’s Birthday. Birnbaum’s (1962) classic “On the Foundations of Statistical Inference,” in Breakthroughs in Statistics (volume I 1993), concerns a principle that remains at the heart of today’s controversies in statistics–even if it isn’t obvious at first: the Likelihood Principle (LP) (also called the strong likelihood Principle SLP, to distinguish it from the weak LP [1]). According to the LP/SLP, given the statistical model, the information from the data are fully contained in the likelihood ratio. Thus, properties of the sampling distribution of the test statistic vanish (as I put it in my slides from my last post)! But error probabilities are all properties of the sampling distribution. Thus, embracing the LP (SLP) blocks our error statistician’s direct ways of taking into account “biasing selection effects” (slide #10).

Intentions is a New Code Word: Where, then, is all the information regarding your trying and trying again, stopping when the data look good, cherry picking, barn hunting and data dredging? For likelihoodists and other probabilists who hold the LP/SLP, it is ephemeral information locked in your head reflecting your “intentions”!  “Intentions” is a code word for “error probabilities” in foundational discussions, as in “who would want to take intentions into account?” (Replace “intentions” (or the “researcher’s intentions”) with “error probabilities” (or the method’s error probabilities”) and you get a more accurate picture.) Keep this deciphering tool firmly in mind as you read criticisms of methods that take error probabilities into account[2]. For error statisticians, this information reflects real and crucial properties of your inference procedure.

Continue reading →

Categories: Birnbaum, Birnbaum Brakes, frequentist/Bayesian, Likelihood Principle, phil/history of stat, Statistics | 48 Comments

Oxford Gaol: Statistical Bogeymen

Posted on October 31, 2014 by Mayo

Memory Lane: 3 years ago. Oxford Jail (also called Oxford Castle) is an entirely fitting place to be on (and around) Halloween! Moreover, rooting around this rather lavish set of jail cells (what used to be a single cell is now a dressing room) is every bit as conducive to philosophical reflection as is exile on Elba! (It is now a boutique hotel, though many of the rooms are still too jail-like for me.)  My goal (while in this gaol—as the English sometimes spell it) is to try and free us from the bogeymen and bogeywomen often associated with “classical” statistics. As a start, the very term “classical statistics” should, I think, be shelved, not that names should matter.

In appraising statistical accounts at the foundational level, we need to realize the extent to which accounts are viewed through the eyeholes of a mask or philosophical theory.  Moreover, the mask some wear while pursuing this task might well be at odds with their ordinary way of looking at evidence, inference, and learning. In any event, to avoid non-question-begging criticisms, the standpoint from which the appraisal is launched must itself be independently defended.   But for (most) Bayesian critics of error statistics the assumption that uncertain inference demands a posterior probability for claims inferred is thought to be so obvious as not to require support. Critics are implicitly making assumptions that are at odds with the frequentist statistical philosophy. In particular, they assume a certain philosophy about statistical inference (probabilism), often coupled with the allegation that error statistical methods can only achieve radical behavioristic goals, wherein all that matters are long-run error rates (of some sort)Unknown-2

Criticisms then follow readily: the form of one or both:

  • Error probabilities do not supply posterior probabilities in hypotheses, interpreted as if they do (and some say we just can’t help it), they lead to inconsistencies
  • Methods with good long-run error rates can give rise to counterintuitive inferences in particular cases.
  • I have proposed an alternative philosophy that replaces these tenets with different ones:
  • the role of probability in inference is to quantify how reliably or severely claims (or discrepancies from claims) have been tested
  • the severity goal directs us to the relevant error probabilities, avoiding the oft-repeated statistical fallacies due to tests that are overly sensitive, as well as those insufficiently sensitive to particular errors.
  • Control of long run error probabilities, while necessary is not sufficient for good tests or warranted inferences.

Continue reading →

Categories: 3-year memory lane, Bayesian/frequentist, Philosophy of Statistics, Statistics | Tags: , | 30 Comments

Statistical Science: The Likelihood Principle issue is out…!

Posted on September 6, 2014 by Mayo

Stat SciAbbreviated Table of Contents:

Table of ContentsHere are some items for your Saturday-Sunday reading. 

Link to complete discussion: 

Mayo, Deborah G. On the Birnbaum Argument for the Strong Likelihood Principle (with discussion & rejoinder). Statistical Science 29 (2014), no. 2, 227-266.

Links to individual papers:

Mayo, Deborah G. On the Birnbaum Argument for the Strong Likelihood Principle. Statistical Science 29 (2014), no. 2, 227-239.

Dawid, A. P. Discussion of “On the Birnbaum Argument for the Strong Likelihood Principle”. Statistical Science 29 (2014), no. 2, 240-241.

Evans, Michael. Discussion of “On the Birnbaum Argument for the Strong Likelihood Principle”. Statistical Science 29 (2014), no. 2, 242-246.

Martin, Ryan; Liu, Chuanhai. Discussion: Foundations of Statistical Inference, Revisited. Statistical Science 29 (2014), no. 2, 247-251.

Fraser, D. A. S. Discussion: On Arguments Concerning Statistical Principles. Statistical Science 29 (2014), no. 2, 252-253.

Hannig, Jan. Discussion of “On the Birnbaum Argument for the Strong Likelihood Principle”. Statistical Science 29 (2014), no. 2, 254-258.

Bjørnstad, Jan F. Discussion of “On the Birnbaum Argument for the Strong Likelihood Principle”. Statistical Science 29 (2014), no. 2, 259-260.

Mayo, Deborah G. Rejoinder: “On the Birnbaum Argument for the Strong Likelihood Principle”. Statistical Science 29 (2014), no. 2, 261-266.

Abstract: An essential component of inference based on familiar frequentist notions, such as p-values, significance and confidence levels, is the relevant sampling distribution. This feature results in violations of a principle known as the strong likelihood principle (SLP), the focus of this paper. In particular, if outcomes x∗ and y∗ from experiments E1 and E2 (both with unknown parameter θ), have different probability models f1( . ), f2( . ), then even though f1(x∗; θ) = cf2(y∗; θ) for all θ, outcomes x∗ and y∗may have different implications for an inference about θ. Although such violations stem from considering outcomes other than the one observed, we argue, this does not require us to consider experiments other than the one performed to produce the data. David Cox [Ann. Math. Statist. 29 (1958) 357–372] proposes the Weak Conditionality Principle (WCP) to justify restricting the space of relevant repetitions. The WCP says that once it is known which Ei produced the measurement, the assessment should be in terms of the properties of Ei. The surprising upshot of Allan Birnbaum’s [J.Amer.Statist.Assoc.57(1962) 269–306] argument is that the SLP appears to follow from applying the WCP in the case of mixtures, and so uncontroversial a principle as sufficiency (SP). But this would preclude the use of sampling distributions. The goal of this article is to provide a new clarification and critique of Birnbaum’s argument. Although his argument purports that [(WCP and SP), entails SLP], we show how data may violate the SLP while holding both the WCP and SP. Such cases also refute [WCP entails SLP].

Key words: Birnbaumization, likelihood principle (weak and strong), sampling theory, sufficiency, weak conditionality

Regular readers of this blog know that the topic of the “Strong Likelihood Principle (SLP)” has come up quite frequently. Numerous informal discussions of earlier attempts to clarify where Birnbaum’s argument for the SLP goes wrong may be found on this blog. [SEE PARTIAL LIST BELOW.[i]] These mostly stem from my initial paper Mayo (2010) [ii]. I’m grateful for the feedback.

In the months since this paper has been accepted for publication, I’ve been asked, from time to time, to reflect informally on the overall journey: (1) Why was/is the Birnbaum argument so convincing for so long? (Are there points being overlooked, even now?) (2) What would Birnbaum have thought? (3) What is the likely upshot for the future of statistical foundations (if any)?

I’ll try to share some responses over the next week. (Naturally, additional questions are welcome.)

[i] A quick take on the argument may be found in the appendix to: “A Statistical Scientist Meets a Philosopher of Science: A conversation between David Cox and Deborah Mayo (as recorded, June 2011)”

 UPhils and responses

 

 

Categories: Birnbaum, Birnbaum Brakes, frequentist/Bayesian, Likelihood Principle, phil/history of stat, Statistics | 40 Comments

BREAKING THE LAW! (of likelihood): to keep their fit measures in line (A), (B 2nd)

Posted on August 29, 2014 by Mayo
.

.

1.An Assumed Law of Statistical Evidence (law of likelihood)

Nearly all critical discussions of frequentist error statistical inference (significance tests, confidence intervals, p- values, power, etc.) start with the following general assumption about the nature of inductive evidence or support:

Data x are better evidence for hypothesis H1 than for H0 if x are more probable under H1 than under H0.

Ian Hacking (1965) called this the logic of support: x supports hypotheses H1 more than H0 if H1 is more likely, given x than is H0:

Pr(x; H1) > Pr(x; H0).

[With likelihoods, the data x are fixed, the hypotheses vary.]*

Or,

x is evidence for H1 over H0 if the likelihood ratio LR (H1 over H0 ) is greater than 1.

It is given in other ways besides, but it’s the same general idea. (Some will take the LR as actually quantifying the support, others leave it qualitative.)

In terms of rejection:

“An hypothesis should be rejected if and only if there is some rival hypothesis much better supported [i.e., much more likely] than it is.” (Hacking 1965, 89)

2. Barnard (British Journal of Philosophy of Science )

But this “law” will immediately be seen to fail on our minimal severity requirement. Hunting for an impressive fit, or trying and trying again, it’s easy to find a rival hypothesis H1 much better “supported” than H0 even when H0 is true. Or, as Barnard (1972) puts it, “there always is such a rival hypothesis, viz. that things just had to turn out the way they actually did” (1972 p. 129).  H0: the coin is fair, gets a small likelihood (.5)k given k tosses of a coin, while H1: the probability of heads is 1 just on those tosses that yield a head, renders the sequence of k outcomes maximally likely. This is an example of Barnard’s “things just had to turn out as they did”. Or, to use an example with P-values: a statistically significant difference, being improbable under the null H0 , will afford high likelihood to any number of explanations that fit the data well.

3.Breaking the law (of likelihood) by going to the “second,” error statistical level:

How does it fail our severity requirement? First look at what the frequentist error statistician must always do to critique an inference: she must consider the capability of the inference method that purports to provide evidence for a claim. She goes to a higher level or metalevel, as it were. In this case, the likelihood ratio plays the role of the needed statistic d(X). To put it informally, she asks:

What’s the probability the method would yield an LR disfavoring H0 compared to some alternative H1  even if H0 is true?

Continue reading →

Categories: highly probable vs highly probed, law of likelihood, Likelihood Principle, Statistics | 72 Comments

Egon Pearson’s Heresy

Posted on August 11, 2014 by Mayo

E.S. Pearson: 11 Aug 1895-12 June 1980.

Today is Egon Pearson’s birthday: 11 August 1895-12 June, 1980.
E. Pearson rejected some of the familiar tenets that have come to be associated with Neyman and Pearson (N-P) statistical tests, notably the idea that the essential justification for tests resides in a long-run control of rates of erroneous interpretations–what he termed the “behavioral” rationale of tests. In an unpublished letter E. Pearson wrote to Birnbaum (1974), he talks about N-P theory admitting of two interpretations: behavioral and evidential:

“I think you will pick up here and there in my own papers signs of evidentiality, and you can say now that we or I should have stated clearly the difference between the behavioral and evidential interpretations. Certainly we have suffered since in the way the people have concentrated (to an absurd extent often) on behavioral interpretations”.

(Nowadays, some people concentrate to an absurd extent on “science-wise error rates in dichotomous screening”.)

When Erich Lehmann, in his review of my “Error and the Growth of Experimental Knowledge” (EGEK 1996), called Pearson “the hero of Mayo’s story,” it was because I found in E.S.P.’s work, if only in brief discussions, hints, and examples, the key elements for an “inferential” or “evidential” interpretation of N-P statistics. Granted, these “evidential” attitudes and practices have never been explicitly codified to guide the interpretation of N-P tests. If they had been, I would not be on about providing an inferential philosophy all these years.[i] Nevertheless, “Pearson and Pearson” statistics (both Egon, not Karl) would have looked very different from Neyman and Pearson statistics, I suspect. One of the few sources of E.S. Pearson’s statistical philosophy is his (1955) “Statistical Concepts in Their Relation to Reality”. It begins like this: Continue reading →

Categories: phil/history of stat, Philosophy of Statistics, Statistics | Tags: , | 2 Comments

“Statistical Science and Philosophy of Science: where should they meet?”

Posted on June 14, 2014 by Mayo

img_1142

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 →

Categories: Error Statistics, Philosophy of Statistics, Severity, Statistics, StatSci meets PhilSci | 23 Comments

Allan Birnbaum, Philosophical Error Statistician: 27 May 1923 – 1 July 1976

Posted on May 27, 2014 by Mayo

27 May 1923-   1 July 1976

Today is Allan Birnbaum’s Birthday. Birnbaum’s (1962) classic “On the Foundations of Statistical Inference” is in Breakthroughs in Statistics (volume I 1993).  I’ve a hunch that Birnbaum would have liked my rejoinder to discussants of my forthcoming paper (Statistical Science): Bjornstad, Dawid, Evans, Fraser, Hannig, and Martin and Liu. I hadn’t realized until recently that all of this is up under “future papers” here [1]. You can find the rejoinder: STS1404-004RA0-2. That takes away some of the surprise of having it all come out at once (and in final form). For those unfamiliar with the argument, at the end of this entry are slides from a recent, entirely informal, talk that I never posted, as well as some links from this blog. Happy Birthday Birnbaum! Continue reading →

Categories: Birnbaum, Birnbaum Brakes, Likelihood Principle, Statistics | Leave a comment

Deconstructing Andrew Gelman: “A Bayesian wants everybody else to be a non-Bayesian.”

Posted on May 17, 2014 by Mayo

At the start of our seminar, I said that “on weekends this spring (in connection with Phil 6334, but not limited to seminar participants) I will post some of my ‘deconstructions‘ of articles”. I began with Andrew Gelman‘s note  “Ethics and the statistical use of prior information”[i], but never posted my deconstruction of it. So since it’s Saturday night, and the seminar is just ending, here it is, along with related links to Stat and ESP research (including me, Jack Good, Persi Diaconis and Pat Suppes). Please share comments especially in relation to current day ESP research. Continue reading →

Categories: Background knowledge, Gelman, Phil6334, Statistics | 35 Comments

Severe osteometric probing of skeletal remains: John Byrd

Posted on March 28, 2014 by Mayo

images-3John E. Byrd, Ph.D. D-ABFA

Central Identification Laboratory
JPAC

Guest, March 27, PHil 6334

“Statistical Considerations of the Histomorphometric Test Protocol for Determination of Human Origin of Skeletal Remains”

 By:
Byrd 1John E. Byrd, Ph.D. D-ABFA
Maria-Teresa Tersigni-Tarrant, Ph.D.
Central Identification Laboratory
JPAC

Categories: Phil6334, Philosophy of Statistics, Statistics | 1 Comment

The Unexpected Way Philosophy Majors Are Changing The World Of Business

Posted on March 25, 2014 by Mayo

 

Philosopher

Philosopher

“Philosophy majors rule” according to this recent article. We philosophers should be getting the word out. Admittedly, the type of people inclined to do well in philosophy are already likely to succeed in analytic areas. Coupled with the chuzpah of taking up an “outmoded and impractical” major like philosophy in the first place, innovative tendencies are not surprising.  But can the study of philosophy also promote these capacities? I think it can and does; yet it could be far more effective than it is, if it was less hermetic and more engaged with problem-solving across the landscape of science,statistics,law,medicine,and evidence-based policy. Here’s the article: Continue reading →

Categories: philosophy of science, Philosophy of Statistics, Statistics | 1 Comment

Significance tests and frequentist principles of evidence: Phil6334 Day #6

Posted on March 2, 2014 by Mayo

picture-216-1Slides (2 sets) from Phil 6334 2/27/14 class (Day#6).

spanos

D. Mayo:
“Frequentist Statistics as a Theory of Inductive Inference”

A. Spanos
“Probability/Statistics Lecture Notes 4: Hypothesis Testing”

Categories: P-values, Phil 6334 class material, Philosophy of Statistics, Statistics | Tags: | Leave a comment

Phil 6334: February 20, 2014 (Spanos): Day #5

Posted on February 24, 2014 by Mayo

may-4-8-aris-spanos-e2809contology-methodology-in-statistical-modelinge2809dPHIL 6334 – “Probability/Statistics Lecture Notes 3 for 2/20/14: Estimation (Point and Interval)”:(Prof. Spanos)*

*This is Day #5 on the Syllabus, as Day #4 had to be made up (Feb 24, 2014) due to snow. Slides for Day #4 will go up Feb. 26, 2014. (See the revised Syllabus Second Installment.)

Categories: Phil6334, Philosophy of Statistics, Spanos | 5 Comments

Phil 6334: Day #2 Slides

Posted on January 31, 2014 by Mayo

 

Picture 216 1mayo Day #2, Part 1: D. Mayo: 

Class, Part 2: A. Spanos:picture-072-1-1
Probability/Statistics Lecture Notes 1: Introduction to Probability and Statistical Inference

Day #1 slides are here.

Categories: Phil 6334 class material, Philosophy of Statistics, Statistics | 8 Comments

BOSTON COLLOQUIUM FOR PHILOSOPHY OF SCIENCE: Revisiting the Foundations of Statistics

Posted on January 29, 2014 by Mayo

BOSTON COLLOQUIUM FOR PHILOSOPHY OF SCIENCE

2013–2014
54th Annual Program

Download the 54th Annual Program

REVISITING THE FOUNDATIONS OF STATISTICS IN THE ERA OF BIG DATA: SCALING UP TO MEET THE CHALLENGE

Cosponsored by the Department of Mathematics & Statistics at Boston University.
Friday, February 21, 2014
10 a.m. – 5:30 p.m.
Photonics Center, 9th Floor Colloquium Room (Rm 906)
8 St. Mary’s Street

10 a.m.–noon

  • Computational Challenges in Genomic Medicine
    Jill Mesirov Computational Biology and Bioinformatics, Broad Institute
  • Selection, Significance, and Signification: Issues in High Energy Physics
    Kent Staley Philosophy, Saint Louis University

1:30–5:30 p.m.

  • Multi-Resolution Inference: An Engineering (Engineered?) Foundation of Statistical Inference
    Xiao-Li Meng Statistics, Harvard University
  • Is the Philosophy of Probabilism an Obstacle to Statistical Fraud Busting?
    Deborah Mayo Philosophy, Virginia Tech
  • Targeted Learning from Big Data
    Mark van der Laan Biostatistics and Statistics, UC Berkeley

Panel Discussion

Boston Colloquium 2013-2014 (3)

Categories: Announcement, philosophy of science, Philosophy of Statistics, Statistical fraudbusting, Statistics | Leave a comment

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