SIST

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 | 1 Comment

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