Review of Error and Inference

“A sense of security regarding the future of statistical science…” Anon review of Error and Inference

errorinferencebookcover-e1335149598836-1

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Aris Spanos, my colleague (in economics) and co-author, came across this anonymous review of our Error and Inference (2010) [E & I]. Interestingly, the reviewer remarks that “The book gives a sense of security regarding the future of statistical science and its importance in many walks of life.” We’re not sure what the reviewer means–but it’s appreciated regardless. This post was from yesterday’s 3-year memory lane and was first posted here.

2010 American Statistical Association and the American Society for Quality

TECHNOMETRICS, AUGUST 2010, VOL. 52, NO. 3, Book Reviews, 52:3, pp. 362-370.

Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, edited by Deborah G. MAYO and Aris SPANOS, New York: Cambridge University Press, 2010, ISBN 978-0-521-88008-4, xvii+419 pp., $60.00.

This edited volume contemplates the interests of both scientists and philosophers regarding gathering reliable information about the problem/question at hand in the presence of error, uncertainty, and with limited data information.

The volume makes a significant contribution in bridging the gap between scientific practice and the philosophy of science. The main contribution of this volume pertains to issues of error and inference, and showcases intriguing discussions on statistical testing and providing alternative strategy to Bayesian inference. In words, it provides cumulative information towards the philosophical and methodological issues of scientific inquiry at large.

The target audience of this volume is quite general and open to a broad readership. With some reasonable knowledge of probability theory and statistical science, one can get the maximum benefit from most of the chapters of the volume. The volume contains original and fascinating articles by eminent scholars (nine, including the editors) who range from names in statistical science to philosophy, including D. R. Cox, a name well known to statisticians. Continue reading

Categories: 3-year memory lane, Review of Error and Inference, Statistics | 3 Comments

Two Severities? (PhilSci and PhilStat)

Janus--2faceThe 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.)

Categories: Review of Error and Inference, Severity, StatSci meets PhilSci | 52 Comments

“A sense of security regarding the future of statistical science…” Anon review of Error and Inference

errorinferencebookcover-e1335149598836-1Aris Spanos, my colleague and co-author (Economics),recently came across this seemingly anonymous review of our Error and Inference (2010) [E & I]. It’s interesting that the reviewer remarks that “The book gives a sense of security regarding the future of statistical science and its importance in many walks of life.” I wish I knew just what the reviewer means–but it’s appreciated regardless.

2010 American Statistical Association and the American Society for Quality

TECHNOMETRICS, AUGUST 2010, VOL. 52, NO. 3, Book Reviews, 52:3, pp. 362-370.

Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, edited by Deborah G. MAYO and Aris SPANOS, New York: Cambridge University Press, 2010, ISBN 978-0-521-88008-4, xvii+419 pp., $60.00.

This edited volume contemplates the interests of both scientists and philosophers regarding gathering reliable information about the problem/question at hand in the presence of error, uncertainty, and with limited data information.

The volume makes a significant contribution in bridging the gap between scientific practice and the philosophy of science. The main contribution of this volume pertains to issues of error and inference, and showcases intriguing discussions on statistical testing and providing alternative strategy to Bayesian inference. In words, it provides cumulative information towards the philosophical and methodological issues of scientific inquiry at large.

The target audience of this volume is quite general and open to a broad readership. With some reasonable knowledge of probability theory and statistical science, one can get the maximum benefit from most of the chapters of the volume. The volume contains original and fascinating articles by eminent scholars (nine, including the editors) who range from names in statistical science to philosophy, including D. R. Cox, a name well known to statisticians.

The editors have done a superb job in presenting, organizing, and structuring the material in a logical order. The “Introduction and Background” is nicely presented and summarized, allowing for a smooth reading of the rest of the volume. There is a broad range of carefully selected topics from various related fields reflecting recent developments in these areas. The rest of the volume is divided in nine chapters/sections as follows:

1. Learning from Error, Severe Testing, and the Growth of Theoretical

Knowledge

2. The Life of Theory in the New Experimentalism

3. Revisiting Critical Rationalism

4. Theory Confirmation and Novel Evidence

5. Induction and Severe Testing

6. Theory Testing in Economics and the Error-Statistical Perspective

7. New Perspectives on (Some Old) Problems of Frequentist Statistics

8. Casual Modeling, Explanation and Severe Testing

9. Error and Legal Epistemology

In summary, this volume contains a wealth of knowledge and fascinating debates on a host of important and controversial topics equally important to the philosophy of science and scientific practice. This is a must-read—I enjoyed reading it and I am sure you will too! The book gives a sense of security regarding the future of statistical science and its importance in many walks of life. I also want to take the opportunity to suggest another seemingly related book by Harman and Kulkarni (2007). The review of this book was appeared in Technometricsin May 2008 (Ahmed 2008).

The following are chapters in E & I (2010) written by Mayo and/or Spanos, if you’re interested. If you produce a palindrome meeting the extremely simple requirements for May (by May 25 or so), you can win a free copy! Continue reading

Categories: Review of Error and Inference, Statistics | 3 Comments

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