# Birnbaum

## Next Phil Stat Forum: January 7: D. Mayo: Putting the Brakes on the Breakthrough (or “How I used simple logic to uncover a flaw in …..statistical foundations”)

## A Perfect Time to Binge Read the (Strong) Likelihood Principle

An essential component of inference based on familiar frequentist notions: p-values, significance and confidence levels, is the relevant sampling distribution (hence the term *sampling theory, *or my preferred *error statistics, *as we get error probabilities from the sampling distribution). This feature results in violations of a principle known as the *strong likelihood principle* (SLP). To state the SLP roughly, it asserts that all the evidential import in the data (for parametric inference within a model) resides in the likelihoods. If accepted, it would render error probabilities irrelevant post data. Continue reading

## Cox’s (1958) Chestnut: You should not get credit (or blame) for something you didn’t do

Just as you keep up your physical exercise during the pandemic (*sure*), you want to keep up with mental gymnastics too. With that goal in mind, and given we’re just a few days from the New Year (and given especially my promised presentation for January 7), here’s one of the two simple examples that will limber you up for the puzzle to ensue. It’s the famous weighing machine example from Sir David Cox (1958)[1]. It is one of the “chestnuts” in the museum exhibits of “chestnuts and howlers” in Excursion 3 (Tour II) of my book *Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars* (SIST, 2018). So block everything else out for a few minutes and consider 3 pages from SIST … Continue reading

## Next Phil Stat Forum: January 7: D. Mayo: Putting the Brakes on the Breakthrough (or “How I used simple logic to uncover a flaw in …..statistical foundations”)

## Birthday of Allan Birnbaum: Foundations of Probability and Statistics (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 had posted the volume before, but there are several articles that are very worth rereading. 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.)** Continue reading

## A Perfect Time to Binge Read the (Strong) Likelihood Principle

An essential component of inference based on familiar frequentist notions: p-values, significance and confidence levels, is the relevant sampling distribution (hence the term *sampling theory, *or my preferred *error statistics, *as we get error probabilities from the sampling distribution). This feature results in violations of a principle known as the *strong likelihood principle* (SLP). To state the SLP roughly, it asserts that all the evidential import in the data (for parametric inference within a model) resides in the likelihoods. If accepted, it would render error probabilities irrelevant post data. Continue reading

## Cox’s (1958) Chestnut: You shouldn’t get credit (or blame) for something you didn’t do

Just as you regularly keep up your physical exercise during the pandemic (sure), you also want to keep up with brain exercise. Given we’re just a few days from New Year’s eve, and given especially that on January 7 I will attempt (for the first time) a highly informal presentation of a controversial result in statistical foundations), here’s a little 2018 marked 60 years since the famous weighing machine example from Sir David Cox (1958)[1]. it is now 61. It’s one of the “chestnuts” in the exhibits of “chestnuts and howlers” in Excursion 3 (Tour II) of my (still) new book *Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars* (SIST, 2018). It’s especially relevant to take this up now, just before we leave 2019, for reasons that will be revealed over the next day or two. For a sneak preview of those reasons, see the “note to the reader” at the end of this post. So, let’s go back to it, with an excerpt from SIST (pp. 170-173). Continue reading

## 60 Years of Cox’s (1958) Chestnut: Excerpt from Excursion 3 Tour II (Mayo 2018, CUP)

2018 marked 60 years since the famous weighing machine example from Sir David Cox (1958)[1]. It’s one of the “chestnuts” in the exhibits of “chestnuts and howlers” in Excursion 3 (Tour II) of my new book *Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars* (SIST). It’s especially relevant to take this up now, just before we leave 2018, for reasons that will be revealed over the next day or two. So, let’s go back to it, with an excerpt from SIST (pp. 170-173).

**Exhibit (vi): Two Measuring Instruments of Different Precisions. ***Did you hear about the frequentist who, knowing she used a scale that’s right only half the time, claimed her method of weighing is right 75% of the time?*

She says, “I flipped a coin to decide whether to use a scale that’s right 100% of the time, or one that’s right only half the time, so, overall, I’m right 75% of the time.” (She wants credit because she could have used a better scale, even knowing she used a lousy one.)

*Basis for the joke: *An N-P test bases error probability on all possible outcomes or measurements that could have occurred in repetitions, but did not. Continue reading

## “Intentions (in your head)” is the code word for “error probabilities (of a procedure)”: Allan Birnbaum’s Birthday

**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 this 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). [Posted earlier here.] Interesting, as seen in a 2018 post on Neyman, Neyman *did* discuss this paper, but had an odd reaction that I’m not sure I understand. (Check it out.) Continue reading

## Allan Birnbaum: Foundations of Probability and Statistics (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

Syntheseon 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 ofSynthesein 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

## A. Birnbaum: Statistical Methods in Scientific Inference (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

## Allan Birnbaum: Foundations of Probability and Statistics (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

Syntheseon 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 ofSynthesein 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

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

**Today is Allan Birnbaum’s Birthday. B**irnbaum’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.

## Statistical Science: The Likelihood Principle issue is out…!

**Abbreviated Table of Contents:**

Here 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 *E*_{1} and *E*_{2} (both with unknown parameter *θ*), have different probability models* f*_{1}( . ),* f*_{2}( . ), then even though *f*_{1}(*x*^{∗}; *θ*) = c*f*_{2}(*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 *E _{i}* produced the measurement, the assessment should be in terms of the properties of

*E*. 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].

_{i}**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)”

- Midnight with Birnbaum (Happy New Year).
- New Version: On the Birnbaum argument for the SLP: Slides for my JSM talk.
- Don’t Birnbaumize that experiment my friend*–updated reblog.
- Allan Birnbaum, Philosophical Error Statistician: 27 May 1923 – 1 July 1976 .
- LSE seminar
- A. Birnbaum: Statistical Methods in Scientific Inference
- ReBlogging the Likelihood Principle #2: Solitary Fishing: SLP Violations
- Putting the brakes on the breakthrough: An informal look at the argument for the Likelihood Principle.

- Forthcoming paper on the strong likelihood principle.

**UPhils and responses**

- U-PHIL: Gandenberger & Hennig : Blogging Birnbaum’s Proof
- U-Phil: Mayo’s response to Hennig and Gandenberger
- Mark Chang (now) gets it right about circularity
- U-Phil: Ton o’ Bricks
- Blogging (flogging?) the SLP: Response to Reply- Xi’an Robert
- U-Phil: J. A. Miller: Blogging the SLP

- [ii]
- Mayo, D. G. (2010). “An Error in the Argument from Conditionality and Sufficiency to the Likelihood Principle” in
*Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability and the Objectivity and Rationality of Science*(D Mayo and A. Spanos eds.), Cambridge: Cambridge University Press: 305-14.

- Mayo, D. G. (2010). “An Error in the Argument from Conditionality and Sufficiency to the Likelihood Principle” in

## Allan Birnbaum, Philosophical Error Statistician: 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