SIST

Graduate Research Seminar: Current Controversies in Phil Stat: LSE PH 500: 21 May – 18 June 2020

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Ship StatInfasST will embark on a new journey from 21 May – 18 June, a graduate research seminar for the Philosophy, Logic & Scientific Method Department at the LSE, but given the pandemic has shut down cruise ships, it will remain at dock in the U.S. and use zoom. If you care to follow any of the 5 sessions, nearly all of the materials will be linked here collected from excerpts already on this blog. If you are interested in observing on zoom beginning 28 May, please follow the directions here

For the updated schedule, see the seminar web page.

Topic: Current Controversies in Phil Stat
(LSE, Remote 10am-12 EST, 15:00 – 17:00 London time; Thursdays 21 May-18 June)

Main Text SIST: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars CUP, 2018):

I. (May 21)  Introduction: Controversies in Phil Stat:  

SIST: Preface, Excursion 1
Preface
Excursion 1 Tour I
Excursion 1 Tour II

Notes/Outline of Excursion 1
Postcard: Souvenir A

II. (May 28) N-P and Fisherian Tests, Severe Testing:

SIST: Excursion 3 Tour I (focus on pages up to p. 152)

Recommended: Excursion 2 Tour II pp. 92-100

Optional: I will (try to) answer questions on demarcation of science, induction, falsification, Popper from Excursion 2 Tour II

Handout: Areas Under the Standard Normal Curve

III. (June 4) Deeper Concepts: Confidence Intervals and Tests: Higgs’ Discovery:

SIST: Excursion 3 Tour III

Optional: I will answer questions on Excursion 3 Tour II: Howlers and Chestnuts of Tests 

IV. (June 11) Rejection Fallacies: Do P-values exaggerate evidence?
      Jeffreys-Lindley paradox or Bayes/Fisher disagreement:

SIST: Excursion 4 Tour II

           SIST: Excursion 4 Tour II

          Recommended (if time)Excursion 4 Tour I: The Myth of “The Myth of Objectivity” 

V. (June 18) The Statistics Wars and Their Casualties:

SIST: Excursion 4 Tour III: pp. 267-286; Farewell Keepsakepp. 436-444
-Amrhein, V., Greenland, S., & McShane, B., (2019). Comment: Retire Statistical Significance, Nature, 567: 305-308.
-Ioannidis J. (2019). “The Importance of Predefined Rules and Prespecified Statistical Analyses: Do Not Abandon Significance.” JAMA. 321(21): 2067–2068. doi:10.1001/jama.2019.4582
-Ioannidis, J. (2019). Correspondence: Retiring statistical significance would give bias a free pass. Nature, 567, 461. https://doi.org/10.1038/d41586-019-00969-2
-Mayo, DG. (2019), P‐value thresholds: Forfeit at your peril. Eur J Clin Invest, 49: e13170. doi: 10.1111/eci.13170

 

Information Items for SIST

-References: Captain’s Bibliography
Souvenirs
-Summaries of 16 Tours (abstracts & keywords)
Excerpts & Mementos on Error Statistics Philosophy Blog (I will link to items from excerpted proofs for interested blog followers as we proceed)
Schaum’s Appendix 2Areas Under the Standard Normal Curve from 0-Z

DELAYED: JUNE 19-20 Workshop: The Statistics Wars and Their Casualties

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The First Eye-Opener: Error Probing Tools vs Logics of Evidence (Excursion 1 Tour II)

1.4, 1.5

In Tour II of this first Excursion of Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (SIST, 2018, CUP),  I pull back the cover on disagreements between experts charged with restoring integrity to today’s statistical practice. Some advised me to wait until later (in the book) to get to this eye-opener. Granted, the full story involves some technical issues, but after many months, I think I arrived at a way to get to the heart of things informally (with a promise of more detailed retracing of steps later on). It was too important not to reveal right away that some of the most popular “reforms” fall down on the job even with respect to our most minimal principle of evidence (you don’t have evidence for a claim if little if anything has been done to probe the ways it can be flawed).  Continue reading

Categories: Error Statistics, law of likelihood, SIST | 14 Comments

Excursion 1 Tour II: Error Probing Tools versus Logics of Evidence-Excerpt

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For the first time, I’m excerpting all of Excursion 1 Tour II from SIST (2018, CUP).

1.4 The Law of Likelihood and Error Statistics

If you want to understand what’s true about statistical inference, you should begin with what has long been a holy grail–to use probability to arrive at a type of logic of evidential support–and in the first instance you should look not at full-blown Bayesian probabilism, but at comparative accounts that sidestep prior probabilities in hypotheses. An intuitively plausible logic of comparative support was given by the philosopher Ian Hacking (1965)–the Law of Likelihood. Fortunately, the Museum of Statistics is organized by theme, and the Law of Likelihood and the related Likelihood Principle is a big one. Continue reading

Categories: Error Statistics, law of likelihood, SIST | 2 Comments

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6334/6614: Captain’s Library: Biblio With Links

Mayo and A. Spanos
PHIL 6334/ ECON 6614: Spring 2019: Current Debates on Statistical Inference and Modeling

Bibliography (this includes a selection of articles with links; numbers 1-15 after the item refer to seminar meeting number.)

See Syllabus (first) for class meetings, and the page PhilStat19 menu up top for other course items.

Achinstein (2010). Mill’s Sins or Mayo’s Errors? (E&I: 170-188). (11)

Bacchus, Kyburg, & Thalos (1990). Against Conditionalization, Synthese (85): 475-506. (15)

Barnett (1999). Comparative Statistical Inference (Chapter 6: Bayesian Inference), John Wiley & Sons. (1), (15)

Begley & Ellis (2012) Raise standards for preclinical cancer research. Nature 483: 531-533. (10)

Continue reading

Categories: SIST | 1 Comment

The Meaning of My Title: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars

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Excerpts from the Preface:

The Statistics Wars: 

Today’s “statistics wars” are fascinating: They are at once ancient and up to the minute. They reflect disagreements on one of the deepest, oldest, philosophical questions: How do humans learn about the world despite threats of error due to incomplete and variable data? At the same time, they are the engine behind current controversies surrounding high-profile failures of replication in the social and biological sciences. How should the integrity of science be restored? Experts do not agree. This book pulls back the curtain on why. Continue reading

Categories: Announcement, SIST | 2 Comments

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