Thanks to CUP, the electronic version of my book, Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018), is available for free for one more week (through August 31) at this link: https://www.cambridge.org/core/books/statistical-inference-as-severe-testing/D9DF409EF568090F3F60407FF2B973B2 Blurbs of the 16 tours in the book may be found here: blurbs of the 16 tours.
Free access to Statistical Inference as Severe Testing: How to Get Beyond the Stat Wars” (CUP, 2018) for 1 more week
CUP will make the electronic version of my book, Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018), available to access for free from August 1-31 at this link: https://www.cambridge.org/core/books/statistical-inference-as-severe-testing/D9DF409EF568090F3F60407FF2B973B2 However, they will confirm the link closer to August, so check this blog on Aug 1 for any update, if you’re interested. (July 31, the link works!) (August 5, the link is working. Let me know if you have problems getting in.) Blurbs of the 16 tours in the book may be found here: blurbs of the 16 tours.
Here’s a CUP interview from when the book first came out.
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) Continue reading
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
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
Bibliography (this includes a selection of articles with links; numbers 1-15 after the item refer to seminar meeting number.)
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)
The Meaning of My Title: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars
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