Monthly Archives: October 2014

Oxford Gaol: Statistical Bogeymen

Memory Lane: 3 years ago. Oxford Jail (also called Oxford Castle) is an entirely fitting place to be on (and around) Halloween! Moreover, rooting around this rather lavish set of jail cells (what used to be a single cell is now a dressing room) is every bit as conducive to philosophical reflection as is exile on Elba! (It is now a boutique hotel, though many of the rooms are still too jail-like for me.)  My goal (while in this gaol—as the English sometimes spell it) is to try and free us from the bogeymen and bogeywomen often associated with “classical” statistics. As a start, the very term “classical statistics” should, I think, be shelved, not that names should matter.

In appraising statistical accounts at the foundational level, we need to realize the extent to which accounts are viewed through the eyeholes of a mask or philosophical theory.  Moreover, the mask some wear while pursuing this task might well be at odds with their ordinary way of looking at evidence, inference, and learning. In any event, to avoid non-question-begging criticisms, the standpoint from which the appraisal is launched must itself be independently defended.   But for (most) Bayesian critics of error statistics the assumption that uncertain inference demands a posterior probability for claims inferred is thought to be so obvious as not to require support. Critics are implicitly making assumptions that are at odds with the frequentist statistical philosophy. In particular, they assume a certain philosophy about statistical inference (probabilism), often coupled with the allegation that error statistical methods can only achieve radical behavioristic goals, wherein all that matters are long-run error rates (of some sort)Unknown-2

Criticisms then follow readily: the form of one or both:

  • Error probabilities do not supply posterior probabilities in hypotheses, interpreted as if they do (and some say we just can’t help it), they lead to inconsistencies
  • Methods with good long-run error rates can give rise to counterintuitive inferences in particular cases.
  • I have proposed an alternative philosophy that replaces these tenets with different ones:
  • the role of probability in inference is to quantify how reliably or severely claims (or discrepancies from claims) have been tested
  • the severity goal directs us to the relevant error probabilities, avoiding the oft-repeated statistical fallacies due to tests that are overly sensitive, as well as those insufficiently sensitive to particular errors.
  • Control of long run error probabilities, while necessary is not sufficient for good tests or warranted inferences.

Continue reading

Categories: 3-year memory lane, Bayesian/frequentist, Philosophy of Statistics, Statistics | Tags: , | 30 Comments

To Quarantine or not to Quarantine?: Science & Policy in the time of Ebola



 Bioethicist Arthur Caplan gives “7 Reasons Ebola Quarantine Is a Bad, Bad Idea”. I’m interested to know what readers think (I claim no expertise in this area.) My occasional comments are in red. 

“Bioethicist: 7 Reasons Ebola Quarantine Is a Bad, Bad Idea”

In the fight against Ebola some government officials in the U.S. are now managing fear, not the virus. Quarantines have been declared in New York, New Jersey and Illinois. In Connecticut, nine people are in quarantine: two students at Yale; a worker from AmeriCARES; and a West African family.

Many others are or soon will be.

Quarantining those who do not have symptoms is not the way to combat Ebola. In fact it will only make matters worse. Far worse. Why?

  1. Quarantining people without symptoms makes no scientific sense.

They are not infectious. The only way to get Ebola is to have someone vomit on you, bleed on you, share spit with you, have sex with you or get fecal matter on you when they have a high viral load.

How do we know this?

Because there is data going back to 1975 from outbreaks in the Congo, Uganda, Sudan, Gabon, Ivory Coast, South Africa, not to mention current experience in the United States, Spain and other nations.

The list of “the only way to get Ebola” does not suggest it is so extraordinarily difficult to transmit as to imply the policy “makes no scientific sense”. That there is “data going back to 1975” doesn’t tell us how it was analyzed. They may not be infectious today, but…

  1. Quarantine is next to impossible to enforce.

If you don’t want to stay in your home or wherever you are supposed to stay for three weeks, then what? Do we shoot you, Taser you, drag you back into your house in a protective suit, or what?

And who is responsible for watching you 24-7? Quarantine relies on the honor system. That essentially is what we count on when we tell people with symptoms to call 911 or the health department.

It does appear that this hasn’t been well thought through yet. NY Governor Cuomo said that “Doctors Without Borders”, the group that sponsors many of the volunteers, already requires volunteers to “decompress” for three weeks upon return from Africa, and they compensate their doctors during this time (see the above link). The state of NY would fill in for those sponsoring groups that do not offer compensation (at least in NY). Is the existing 3 week decompression period already a clue that they want people cleared before they return to work? Continue reading

Categories: science communication | Tags: | 49 Comments


Hand writing a letter with a goose feather

3 years ago…

MONTHLY MEMORY LANE: 3 years ago: October 2011 (I mark in red 3 posts that seem most apt for general background on key issues in this blog*)

*I indicated I’d begin this new, once-a-month feature at the 3-year anniversary. I will repost and comment on one each month. (I might repost others that I do not comment on, as Oct. 31, 2014). For newcomers, here’s your chance to catch-up; for old timers, this is philosophy: rereading is essential!

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

September 2014: Blog Contents

metablog old fashion typewriterSeptember 2014: Error Statistics Philosophy
Blog Table of Contents 

Compiled by Jean A. Miller

  • (9/30) Letter from George (Barnard)
  • (9/27) Should a “Fictionfactory” peepshow be barred from a festival on “Truth and Reality”? Diederik Stapel says no (rejected post)
  • (9/23) G.A. Barnard: The Bayesian “catch-all” factor: probability vs likelihood
  • (9/21) Statistical Theater of the Absurd: “Stat on a Hot Tin Roof”
  • (9/18) Uncle Sam wants YOU to help with scientific reproducibility!
  • (9/15) A crucial missing piece in the Pistorius trial? (2): my answer (Rejected Post)
  • (9/12) “The Supernal Powers Withhold Their Hands And Let Me Alone”: C.S. Peirce
  • (9/6) Statistical Science: The Likelihood Principle issue is out…!
  • (9/4) All She Wrote (so far): Error Statistics Philosophy Contents-3 years on
  • (9/3) 3 in blog years: Sept 3 is 3rd anniversary of





Categories: Announcement, blog contents, Statistics | Leave a comment

PhilStat/Law: Nathan Schachtman: Acknowledging Multiple Comparisons in Statistical Analysis: Courts Can and Must



The following is from Nathan Schachtman’s legal blog, with various comments and added emphases (by me, in this color). He will try to reply to comments/queries.

“Courts Can and Must Acknowledge Multiple Comparisons in Statistical Analyses”

Nathan Schachtman, Esq., PC * October 14th, 2014

In excluding the proffered testimony of Dr. Anick Bérard, a Canadian perinatal epidemiologist in the Université de Montréal, the Zoloft MDL trial court discussed several methodological shortcomings and failures, including Bérard’s reliance upon claims of statistical significance from studies that conducted dozens and hundreds of multiple comparisons.[i] The Zoloft MDL court was not the first court to recognize the problem of over-interpreting the putative statistical significance of results that were one among many statistical tests in a single study. The court was, however, among a fairly small group of judges who have shown the needed statistical acumen in looking beyond the reported p-value or confidence interval to the actual methods used in a study[1].



A complete and fair evaluation of the evidence in situations as occurred in the Zoloft birth defects epidemiology required more than the presentation of the size of the random error, or the width of the 95 percent confidence interval.  When the sample estimate arises from a study with multiple testing, presenting the sample estimate with the confidence interval, or p-value, can be highly misleading if the p-value is used for hypothesis testing.  The fact of multiple testing will inflate the false-positive error rate. Dr. Bérard ignored the context of the studies she relied upon. What was noteworthy is that Bérard encountered a federal judge who adhered to the assigned task of evaluating methodology and its relationship with conclusions.

*   *   *   *   *   *   *

There is no unique solution to the problem of multiple comparisons. Some researchers use Bonferroni or other quantitative adjustments to p-values or confidence intervals, whereas others reject adjustments in favor of qualitative assessments of the data in the full context of the study and its methods. See, e.g., Kenneth J. Rothman, “No Adjustments Are Needed For Multiple Comparisons,” 1 Epidemiology 43 (1990) (arguing that adjustments mechanize and trivialize the problem of interpreting multiple comparisons). Two things are clear from Professor Rothman’s analysis. First for someone intent upon strict statistical significance testing, the presence of multiple comparisons means that the rejection of the null hypothesis cannot be done without further consideration of the nature and extent of both the disclosed and undisclosed statistical testing. Rothman, of course, has inveighed against strict significance testing under any circumstance, but the multiple testing would only compound the problem.

Second, although failure to adjust p-values or intervals quantitatively may be acceptable, failure to acknowledge the multiple testing is poor statistical practice. The practice is, alas, too prevalent for anyone to say that ignoring multiple testing is fraudulent, and the Zoloft MDL court certainly did not condemn Dr. Bérard as a fraudfeasor[2]. [emphasis mine]

I’m perplexed by this mixture of stances. If you don’t mention the multiple testing for which it is acceptable not to adjust, then you’re guilty of poor statistical practice; but its “too prevalent for anyone to say that ignoring multiple testing is fraudulent”. This appears to claim it’s poor statistical practice if you fail to mention your results are due to multiple testing, but “ignoring multiple testing” (which could mean failing to adjust or, more likely, failing to mention it) is not fraudulent. Perhaps, it’s a questionable research practice QRP. It’s back to “50 shades of grey between QRPs and fraud.”

  […read his full blogpost here]

Previous cases have also acknowledged the multiple testing problem. In litigation claims for compensation for brain tumors for cell phone use, plaintiffs’ expert witness relied upon subgroup analysis, which added to the number of tests conducted within the epidemiologic study at issue. Newman v. Motorola, Inc., 218 F. Supp. 2d 769, 779 (D. Md. 2002), aff’d, 78 Fed. App’x 292 (4th Cir. 2003). The trial court explained:

“[Plaintiff’s expert] puts overdue emphasis on the positive findings for isolated subgroups of tumors. As Dr. Stampfer explained, it is not good scientific methodology to highlight certain elevated subgroups as significant findings without having earlier enunciated a hypothesis to look for or explain particular patterns, such as dose-response effect. In addition, when there is a high number of subgroup comparisons, at least some will show a statistical significance by chance alone.”

I’m going to require, as part of its meaning, that a statistically significant difference not be one due to “chance variability” alone. Then to avoid self contradiction, this last sentence might be put as follows: “when there is a high number of subgroup comparisons, at least some will show purported or nominal or unaudited statistical significance by chance alone. [Which term do readers prefer?] If one hunts down one’s hypothesized comparison in the data, then the actual p-value will not equal, and will generally be greater than, the nominal or unaudited p-value.”

So, I will insert “nominal” where needed below (in red).

Texas Sharpshooter fallacy

Id. And shortly after the Supreme Court decided Daubert, the Tenth Circuit faced the reality of data dredging in litigation, and its effect on the meaning of “significance”:

“Even if the elevated levels of lung cancer for men had been [nominally] statistically significant a court might well take account of the statistical “Texas Sharpshooter” fallacy in which a person shoots bullets at the side of a barn, then, after the fact, finds a cluster of holes and draws a circle around it to show how accurate his aim was. With eight kinds of cancer for each sex there would be sixteen potential categories here around which to “draw a circle” to show a [nominally] statistically significant level of cancer. With independent variables one would expect one statistically significant reading in every twenty categories at a 95% confidence level purely by random chance.”

The Texas sharpshooter fallacy is one of my all time favorites. One purports to be testing the accuracy of his aim, when in fact that is not the process that gave rise to the impressive-looking (nominal) cluster of hits. The results do not warrant inferences about his ability to accurately hit a target, since that hasn’t been well-probed. Continue reading

Categories: P-values, PhilStat Law, Statistics | 12 Comments

Gelman recognizes his error-statistical (Bayesian) foundations


From Gelman’s blog:

“In one of life’s horrible ironies, I wrote a paper “Why we (usually) don’t have to worry about multiple comparisons” but now I spend lots of time worrying about multiple comparisons”

Posted by  on

Exhibit A: [2012] Why we (usually) don’t have to worry about multiple comparisons. Journal of Research on Educational Effectiveness 5, 189-211. (Andrew Gelman, Jennifer Hill, and Masanao Yajima)

Exhibit B: The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time, in press. (Andrew Gelman and Eric Loken) (Shortened version is here.)


The “forking paths” paper, in my reading,  basically argues that mere hypothetical possibilities about what you would or might have done had the data been different (in order to secure a desired interpretation) suffices to alter the characteristics of the analysis you actually did. That’s an error statistical argument–maybe even stronger than what some error statisticians would say. What’s really being condemned are overly flexible ways to move from statistical results to substantive claims. The p-values are illicit when taken to provide evidence for those claims because an actual p-value requires Prob(P < p;Ho) = p (and the actual p-value has become much greater by design). The criticism makes perfect sense if you’re scrutinizing inferences according to how well or severely tested they are. Actual error probabilities are accordingly altered or unable to be calculated. However, if one is going to scrutinize inferences according to severity then the same problematic flexibility would apply to Bayesian analyses, whether or not they have a way to pick up on it. (It’s problematic if they don’t.) I don’t see the magic by which a concern for multiple testing disappears in Bayesian analysis (e.g., in the first paper) except by assuming some prior takes care of it.

See my comment here.

Categories: Error Statistics, Gelman | 17 Comments

BREAKING THE (Royall) LAW! (of likelihood) (C)



With this post, I finally get back to the promised sequel to “Breaking the Law! (of likelihood) (A) and (B)” from a few weeks ago. You might wish to read that one first.* A relevant paper by Royall is here.

Richard Royall is a statistician1 who has had a deep impact on recent philosophy of statistics by giving a neat proposal that appears to settle disagreements about statistical philosophy! He distinguishes three questions:

  • What should I believe?
  • How should I act?
  • Is this data evidence of some claim? (or How should I interpret this body of observations as evidence?)

It all sounds quite sensible– at first–and, impressively, many statisticians and philosophers of different persuasions have bought into it. At least they appear willing to go this far with him on the 3 questions.

How is each question to be answered? According to Royall’s commandments writings, what to believe is captured by Bayesian posteriors; how to act, by a behavioristic, N-P long-run performance. And what method answers the evidential question? A comparative likelihood approach. You may want to reject all of them (as I do),2 but just focus on the last.

Remember with likelihoods, the data x are fixed, the hypotheses vary. A great many critical discussions of frequentist error statistical inference (significance tests, confidence intervals, p- values, power, etc.) start with “the law”. But I fail to see why we should obey it.

To begin with, a report of comparative likelihoods isn’t very useful: H might be less likely than H’, given x, but so what? What do I do with that information? It doesn’t tell me I have evidence against or for either.3 Recall, as well, Hacking’s points here about the variability in the meanings of a likelihood ratio across problems. Continue reading

Categories: law of likelihood, Richard Royall, Statistics | 41 Comments

A (Jan 14, 2014) interview with Sir David Cox by “Statistics Views”

Sir David Cox

Sir David Cox

The original Statistics Views interview is here:

“I would like to think of myself as a scientist, who happens largely to specialise in the use of statistics”– An interview with Sir David Cox


  • Author: Statistics Views
  • Date: 24 Jan 2014
  • Copyright: Image appears courtesy of Sir David Cox

Sir David Cox is arguably one of the world’s leading living statisticians. He has made pioneering and important contributions to numerous areas of statistics and applied probability over the years, of which perhaps the best known is the proportional hazards model, which is widely used in the analysis of survival data. The Cox point process was named after him.

Sir David studied mathematics at St John’s College, Cambridge and obtained his PhD from the University of Leeds in 1949. He was employed from 1944 to 1946 at the Royal Aircraft Establishment, from 1946 to 1950 at the Wool Industries Research Association in Leeds, and from 1950 to 1955 worked at the Statistical Laboratory at the University of Cambridge. From 1956 to 1966 he was Reader and then Professor of Statistics at Birkbeck College, London. In 1966, he took up the Chair position in Statistics at Imperial College Londonwhere he later became Head of the Department of Mathematics for a period. In 1988 he became Warden of Nuffield College and was a member of the Department of Statistics at Oxford University. He formally retired from these positions in 1994 but continues to work in Oxford.

Sir David has received numerous awards and honours over the years. He has been awarded the Guy Medals in Silver (1961) and Gold (1973) by the Royal Statistical Society. He was elected Fellow of the Royal Society of London in 1973, was knighted in 1985 and became an Honorary Fellow of the British Academy in 2000. He is a Foreign Associate of the US National Academy of Sciences and a foreign member of the Royal Danish Academy of Sciences and Letters. In 1990 he won the Kettering Prize and Gold Medal for Cancer Research for “the development of the Proportional Hazard Regression Model” and 2010 he was awarded the Copley Medal by the Royal Society.

He has supervised and collaborated with many students over the years, many of whom are now successful in statistics in their own right such as David Hinkley and Past President of the Royal Statistical Society, Valerie Isham. Sir David has served as President of theBernoulli Society, Royal Statistical Society, and the International Statistical Institute.

This year, Sir David is to turn 90*. Here Statistics Views talks to Sir David about his prestigious career in statistics, working with the late Professor Lindley, his thoughts on Jeffreys and Fisher, being President of the Royal Statistical Society during the Thatcher Years, Big Data and the best time of day to think of statistical methods.

1. With an educational background in mathematics at St Johns College, Cambridge and the University of Leeds, when and how did you first become aware of statistics as a discipline?

I was studying at Cambridge during the Second World War and after two years, one was sent either into the Forces or into some kind of military research establishment. There were very few statisticians then, although it was realised there was a need for statisticians. It was assumed that anybody who was doing reasonably well at mathematics could pick up statistics in a week or so! So, aged 20, I went to the Royal Aircraft Establishment in Farnborough, which is enormous and still there to this day if in a different form, and I worked in the Department of Structural and Mechanical Engineering, doing statistical work. So statistics was forced upon me, so to speak, as was the case for many mathematicians at the time because, aside from UCL, there had been very little teaching of statistics in British universities before the Second World War. Afterwards, it all started to expand.

2. From 1944 to 1946 you worked at the Royal Aircraft Establishment and then from 1946 to 1950 at the Wool Industries Research Association in Leeds. Did statistics have any role to play in your first roles out of university?

Totally. In Leeds, it was largely statistics but also to some extent, applied mathematics because there were all sorts of problems connected with the wool and textile industry in terms of the physics, chemistry and biology of the wool and some of these problems were mathematical but the great majority had a statistical component to them. That experience was not totally uncommon at the time and many who became academic statisticians had, in fact, spent several years working in a research institute first.

3. From 1950 to 1955, you worked at the Statistical Laboratory at Cambridge and would have been there at the same time as Fisher and Jeffreys. The late Professor Dennis Lindley, who was also there at that time, told me that the best people working on statistics were not in the statistics department at that time. What are your memories when you look back on that time and what do you feel were your main achievements?

Lindley was exactly right about Jeffreys and Fisher. They were two great scientists outside statistics – Jeffreys founded modern geophysics and Fisher was a major figure in genetics. Dennis was a contemporary and very impressive and effective. We were colleagues for five years and our children even played together.

The first lectures on statistics I attended as a student consisted of a short course by Harold Jeffreys who had at the time a massive reputation as virtually the inventor of modern geophysics. His Theory of Probability, published first as a monograph in physics was and remains of great importance but, amongst other things, his nervousness limited the appeal of his lectures, to put it gently. I met him personally a couple of times – he was friendly but uncommunicative. When I was later at the Statistical Laboratory in Cambridge, relations between the Director, Dr Wishart and R.A. Fisher had been at a very low ebb for 20 years and contact between the Lab and Fisher was minimal. I hear him speak on three of four occasions, interesting if often rambunctious occasions. To some, Fisher showed great generosity but not to the Statistics Lab, which was sad in view of the towering importance of his work.

“To some, Fisher showed great generosity but not to the Statistics Lab, which was sad in view of the towering importance of his work.”

Continue reading

Categories: Sir David Cox | 3 Comments

Diederik Stapel hired to teach “social philosophy” because students got tired of success stories… or something (rejected post)

Oh My*.images-16

(“But I can succeed as a social philosopher”)

The following is from Retraction Watch. UPDATE: OCT 10, 2014**

Diederik Stapel, the Dutch social psychologist and admitted data fabricator — and owner of 54 retraction notices — is now teaching at a college in the town of Tilburg [i].

According to Omroep Brabant, Stapel was offered the job as a kind of adjunct at Fontys Academy for Creative Industries to teach social philosophy. The site quotes a Nick Welman explaining the rationale for hiring Stapel (per Google Translate):

“It came about because students one after another success story were told from the entertainment industry, the industry which we educate them .”

The students wanted something different.

“They wanted to also focus on careers that have failed. On people who have fallen into a black hole, acquainted with the dark side of fame and success.”

Last month, organizers of a drama festival in The Netherlands cancelled a play co-written by Stapel.

I really think Dean Bon puts the rationale most clearly of all.

…A letter from the school’s dean, Pieter Bon, adds:

We like to be entertained and the length of our lives increases. We seek new ways in which to improve our health and we constantly look for new ways to fill our free time. Fashion and looks are important to us; we prefer sustainable products and we like to play games using smart gadgets. This is why Fontys Academy for Creative Industries exists. We train people to create beautiful concepts, exciting concepts, touching concepts, concepts to improve our quality of life. We train them for an industry in which creativity is of the highest value to a product or service. We educate young people who feel at home in the (digital) world of entertainment and lifestyle, and understand that creativity can also mean business. Creativity can be marketed, it’s as simple as that.

We’re sure Prof. Stapel would agree.

[i] Fontys describes itself thusly: Fontys Academy for Creative Industries (Fontys ACI) in Tilburg has 2500 students working towards a bachelor of Business Administration (International Event, Music & Entertainment Studies and Digital Publishing Studies), a bachelor of Communication (International Event, Music & Entertainment Studies) or a bachelor of Lifestyle (International Lifestyle Studies). Fontys ACI hosts a staff of approximately one hundred (teachers plus support staff) as well as about fifty regular visiting lecturers.

 *I wonder if “social philosophy” is being construed as “extreme postmodernist social epistemology”?  

I guess the students are keen to watch that Fictionfactory Peephole.

**Turns out to have been short-lived. Also admits to sockpuppeting at Retraction watch. Frankly I thought it was more fun to guess who “Paul” was, but they have rules.

[ii} One of my April Fool’s Day posts is turning from part fiction to fact.

Categories: Rejected Posts, Statistics | 9 Comments

Oy Faye! What are the odds of not conflating simple conditional probability and likelihood with Bayesian success stories?


Faye Flam

Congratulations to Faye Flam for finally getting her article published at the Science Times at the New York Times, “The odds, continually updated” after months of reworking and editing, interviewing and reinterviewing. I’m grateful too, that one remark from me remained. Seriously I am. A few comments: The Monty Hall example is simple probability not statistics, and finding that fisherman who floated on his boots at best used likelihoods. I might note, too, that critiquing that ultra-silly example about ovulation and voting–a study so bad they actually had to pull it at CNN due to reader complaints[i]–scarcely required more than noticing the researchers didn’t even know the women were ovulating[ii]. Experimental design is an old area of statistics developed by frequentists; on the other hand, these ovulation researchers really believe their theory, so the posterior checks out.

The article says, Bayesian methods can “crosscheck work done with the more traditional or ‘classical’ approach.” Yes, but on traditional frequentist grounds. What many would like to know is how to cross check Bayesian methods—how do I test your beliefs? Anyway, I should stop kvetching and thank Faye and the NYT for doing the article at all[iii]. Here are some excerpts:

Statistics may not sound like the most heroic of pursuits. But if not for statisticians, a Long Island fisherman might have died in the Atlantic Ocean after falling off his boat early one morning last summer.

Continue reading

Categories: Bayesian/frequentist, Statistics | 47 Comments

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