Monthly Archives: December 2015

Midnight With Birnbaum (Happy New Year)

 Just as in the past 4 years since I’ve been blogging, I revisit that spot in the road at 11p.m., just outside the Elbar Room, get into a strange-looking taxi, and head to “Midnight With Birnbaum”. (The pic on the left is the only blurry image I have of the club I’m taken to.) I wonder if the car will come for me this year, given that my Birnbaum article has been out since 2014… The (Strong) Likelihood Principle–whether or not it is named–remains at the heart of many of the criticisms of Neyman-Pearson (N-P) statistics (and cognate methods). Yet as Birnbaum insisted, the “confidence concept” is the “one rock in a shifting scene” of statistical foundations, insofar as there’s interest in controlling the frequency of erroneous interpretations of data. (See my rejoinder.) Birnbaum bemoaned the lack of an explicit evidential interpretation of N-P methods. Maybe in 2016? Anyway, it’s 6 hrs later here, so I’m about to leave for that spot in the road…

You know how in that (not-so) recent Woody Allen movie, “Midnight in Paris,” the main character (I forget who plays it, I saw it on a plane) is a writer finishing a novel, and he steps into a cab that mysteriously picks him up at midnight and transports him back in time where he gets to run his work by such famous authors as Hemingway and Virginia Wolf?  He is impressed when his work earns their approval and he comes back each night in the same mysterious cab…Well, imagine an error statistical philosopher is picked up in a mysterious taxi at midnight (New Year’s Eve 2011 2012, 2013, 2014, 2015) and is taken back fifty years and, lo and behold, finds herself in the company of Allan Birnbaum.[i] There are a couple of brief (12/31/14 & 15) updates at the end.  

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ERROR STATISTICIAN: It’s wonderful to meet you Professor Birnbaum; I’ve always been extremely impressed with the important impact your work has had on philosophical foundations of statistics.  I happen to be writing on your famous argument about the likelihood principle (LP).  (whispers: I can’t believe this!)

BIRNBAUM: Ultimately you know I rejected the LP as failing to control the error probabilities needed for my Confidence concept.

ERROR STATISTICIAN: Yes, but I actually don’t think your argument shows that the LP follows from such frequentist concepts as sufficiency S and the weak conditionality principle WLP.[ii]  Sorry,…I know it’s famous…

BIRNBAUM:  Well, I shall happily invite you to take any case that violates the LP and allow me to demonstrate that the frequentist is led to inconsistency, provided she also wishes to adhere to the WLP and sufficiency (although less than S is needed).

ERROR STATISTICIAN: Well I happen to be a frequentist (error statistical) philosopher; I have recently (2006) found a hole in your proof,..er…well I hope we can discuss it.

BIRNBAUM: Well, well, well: I’ll bet you a bottle of Elba Grease champagne that I can demonstrate it! Continue reading

Categories: Birnbaum Brakes, Statistics, strong likelihood principle | Tags: , , , | 2 Comments

3 YEARS AGO (DECEMBER 2012): MEMORY LANE

3 years ago...

3 years ago…

MONTHLY MEMORY LANE: 3 years ago: December 2012. I am to mark in red three posts that seem most apt for general background on key issues in this blog [1]. However, posts that are part of a “unit” or group of posts count as one, so I’m not really cheating with the 5 in red. The items in the “green” group can’t be considered “general background” but are just the thing for readers interested in an ongoing episode in philosophy of statistics and law (PhilStat/Law/Stock). The two “purples” (12/8 and 12/31) are about the strong likelihood principle (SLP), one of my favorite topics. Whether I will go to meet Allan Birnbaum on New Year’s Eve (as I have for the past 4 years) is not yet decided.

December 2012

[1] I exclude those reblogged fairly recently. Monthly memory lanes began at the blog’s 3-year anniversary in Sept, 2014.

Categories: 3-year memory lane, Statistics | Leave a comment

Stephen Senn: Double Jeopardy?: Judge Jeffreys Upholds the Law (sequel to the pathetic P-value)[4]

S. Senn

S. Senn

Stephen Senn
Head of Competence Center for Methodology and Statistics (CCMS)
Luxembourg Institute of Health

Double Jeopardy?: Judge Jeffreys Upholds the Law*[4]

“But this could be dealt with in a rough empirical way by taking twice the standard error as a criterion for possible genuineness and three times the standard error for definite acceptance”. Harold Jeffreys(1) (p386)

This is the second of two posts on P-values. In the first, The Pathetic P-Value, I considered the relation of P-values to Laplace’s Bayesian formulation of induction, pointing out that that P-values, whilst they had a very different interpretation, were numerically very similar to a type of Bayesian posterior probability. In this one, I consider their relation or lack of it, to Harold Jeffreys’s radically different approach to significance testing. (An excellent account of the development of Jeffreys’s thought is given by Howie(2), which I recommend highly.)

The story starts with Cambridge philosopher CD Broad (1887-1971), who in 1918 pointed to a difficulty with Laplace’s Law of Succession. Broad considers the problem of drawing counters from an urn containing n counters and supposes that all m drawn had been observed to be white. He now considers two very different questions, which have two very different probabilities and writes: Continue reading

Categories: Jeffreys, P-values, reforming the reformers, Stephen Senn | Tags: | 11 Comments

Gelman on ‘Gathering of philosophers and physicists unaware of modern reconciliation of Bayes and Popper’

 I’m reblogging Gelman’s post today: “Gathering of philosophers and physicists unaware of modern reconciliation of Bayes and Popper”. I concur with Gelman’s arguments against all Bayesian “inductive support” philosophies, and welcome the Gelman and Shalizi (2013) ‘meeting of the minds’ between an error statistical philosophy and Bayesian falsification (which I regard as a kind of error statistical Bayesianism). Just how radical a challenge these developments pose to other stripes of Bayesianism has yet to be explored. My comment on them is here.

Screen Shot 2015-12-16 at 11.17.09 PM

“Gathering of philosophers and physicists unaware of modern reconciliation of Bayes and Popper” by Andrew Gelman

Hiro Minato points us to a news article by physicist Natalie Wolchover entitled “A Fight for the Soul of Science.”

I have no problem with most of the article, which is a report about controversies within physics regarding the purported untestability of physics models such as string theory (as for example discussed by my Columbia colleague Peter Woit). Wolchover writes:

Whether the fault lies with theorists for getting carried away, or with nature, for burying its best secrets, the conclusion is the same: Theory has detached itself from experiment. The objects of theoretical speculation are now too far away, too small, too energetic or too far in the past to reach or rule out with our earthly instruments. . . .

Over three mild winter days, scholars grappled with the meaning of theory, confirmation and truth; how science works; and whether, in this day and age, philosophy should guide research in physics or the other way around. . . .

To social and behavioral scientists, this is all an old old story. Concepts such as personality, political ideology, and social roles are undeniably important but only indirectly related to any measurements. In social science we’ve forever been in the unavoidable position of theorizing without sharp confirmation or falsification, and, indeed, unfalsifiable theories such as Freudian psychology and rational choice theory have been central to our understanding of much of the social world.

But then somewhere along the way the discussion goes astray: Continue reading

Categories: Bayesian/frequentist, Error Statistics, Gelman, Shalizi, Statistics | 20 Comments

Stephen Senn: The pathetic P-value (Guest Post) [3]

S. Senn

S. Senn

Stephen Senn
Head of Competence Center for Methodology and Statistics (CCMS)
Luxembourg Institute of Health

The pathetic P-value* [3]

This is the way the story is now often told. RA Fisher is the villain. Scientists were virtuously treading the Bayesian path, when along came Fisher and gave them P-values, which they gladly accepted, because they could get ‘significance’ so much more easily. Nearly a century of corrupt science followed but now there are signs that there is a willingness to return to the path of virtue and having abandoned this horrible Fisherian complication:

We shall not cease from exploration
And the end of all our exploring
Will be to arrive where we started …

A condition of complete simplicity..

And all shall be well and
All manner of thing shall be well

TS Eliot, Little Gidding

Consider, for example, distinguished scientist David Colquhoun citing the excellent scientific journalist Robert Matthews as follows

“There is an element of truth in the conclusion of a perspicacious journalist:

‘The plain fact is that 70 years ago Ronald Fisher gave scientists a mathematical machine for turning baloney into breakthroughs, and flukes into funding. It is time to pull the plug. ‘

Robert Matthews Sunday Telegraph, 13 September 1998.” [1]

However, this is not a plain fact but just plain wrong. Even if P-values were the guilty ‘mathematical machine’ they are portrayed to be, it is not RA Fisher’s fault. Putting the historical record right helps one to understand the issues better. As I shall argue, at the heart of this is not a disagreement between Bayesian and frequentist approaches but between two Bayesian approaches: it is a conflict to do with the choice of prior distributions[2].

Fisher did not persuade scientists to calculate P-values rather than Bayesian posterior probabilities; he persuaded them that the probabilities that they were already calculating and interpreting as posterior probabilities relied for this interpretation on a doubtful assumption. He proposed to replace this interpretation with one that did not rely on the assumption. Continue reading

Categories: P-values, S. Senn, statistical tests, Statistics | 27 Comments

Beware of questionable front page articles warning you to beware of questionable front page articles (2)

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Such articles have continued apace since this blogpost from 2013. During that time, meta-research, replication studies, statistical forensics and fraudbusting have become popular academic fields in their own right. Since I regard the ‘programme’ (to use a Lakatosian term) as essentially a part of the philosophy and methodology of science, I’m all in favor of it—I employed the term “metastatistics” eons ago–but, as a philosopher, I claim there’s a pressing need for meta-meta-research, i.e., a conceptual, logical, and methodological scrutiny of presuppositions and gaps in meta-level work itself.  There was an issue I raised in the section “But what about the statistics?” below that hasn’t been addressed. I question the way size and power (from statistical hypothesis testing) are employed in a “diagnostics and screening” computation that underlies most “most findings are false” articles. (This is (2) in my new “Let PBP” series, and follows upon my last post, comments in burgandy are added, 12/5/15.)

In this time of government cut-backs and sequester, scientists are under increased pressure to dream up ever new strategies to publish attention-getting articles with eye-catching, but inadequately scrutinized, conjectures. Science writers are under similar pressures, and to this end they have found a way to deliver up at least one fire-breathing, front page article a month. How? By writing minor variations on an article about how in this time of government cut-backs and sequester, scientists are under increased pressure to dream up ever new strategies to publish attention-getting articles with eye-catching, but inadequately scrutinized, conjectures. (I’m prepared to admit that meta-research consciousness raising, like “self help books,” warrant frequent revisiting. Lessons are forgotten, and there are always new users of statistics.)

Thus every month or so we see retreads on why most scientific claims are unreliable, biased, wrong, and not even wrong. Maybe that’s the reason the authors of a recent article in The Economist (“Trouble at the Lab“) remain anonymous. (I realize that is their general policy.)  Continue reading

Categories: junk science, Let PBP, P-values, science-wise screening, Statistics | 23 Comments

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