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

S. Senn

S. Senn

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

The pathetic P-value

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 | 147 Comments

All She Wrote (so far): Error Statistics Philosophy: 3.5 years on

 

metablog old fashion typewriter

D.G. Mayo with typewriter

Error Statistics Philosophy: Blog Contents (3.5 years)
By: D. G. Mayo [i]

September 2011

October 2011

Continue reading

Categories: blog contents, Metablog, Statistics | 1 Comment

A puzzle about the latest test ban (or ‘don’t ask, don’t tell’)

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A large number of people have sent me articles on the “test ban” of statistical hypotheses tests and confidence intervals at a journal called Basic and Applied Social Psychology (BASP)[i]. Enough. One person suggested that since it came so close to my recent satirical Task force post, that I either had advance knowledge or some kind of ESP. Oh please, no ESP required.None of this is the slightest bit surprising, and I’ve seen it before; I simply didn’t find it worth blogging about. Statistical tests are being banned, say the editors, because they purport to give probabilities of null hypotheses (really?) and do not, hence they are “invalid”.[ii] (Confidence intervals are thrown in the waste bin as well—also claimed “invalid”).“The state of the art remains uncertain” regarding inferential statistical procedures, say the editors.  I don’t know, maybe some good will come of all this.

Yet there’s a part of their proposal that brings up some interesting logical puzzles, and logical puzzles are my thing. In fact, I think there is a mistake the editors should remedy, lest authors be led into disingenuous stances, and strange tangles ensue. I refer to their rule that authors be allowed to submit papers whose conclusions are based on allegedly invalid methods so long as, once accepted, they remove any vestiges of them!

Question 1. Will manuscripts with p-values be desk rejected automatically?

Answer to Question 1. No. If manuscripts pass the preliminary inspection, they will be sent out for review. But prior to publication, authors will have to remove all vestiges of the NHSTP (p-values, t-values, F-values, statements about “significant” differences or lack thereof, and so on).”

Now if these measures are alleged to be irrelevant and invalid instruments for statistical inference, then why should they be included in the peer review process at all? Will reviewers be told to ignore them? That would only seem fair: papers should not be judged by criteria alleged to be invalid, but how will reviewers blind themselves to them? It would seem the measures should be excluded from the get-go. If they are included in the review, why shouldn’t the readers see what the reviewers saw when they recommended acceptance?

But here’s where the puzzle really sets in. If the authors must free their final papers from such encumbrances as sampling distributions and error probabilities, what will be the basis presented for their conclusions in the published paper? Presumably, from the notice, they are allowed only mere descriptive statistics or non-objective Bayesian reports (added: actually can’t tell which kind of Bayesianism they allow, given the Fisher reference which doesn’t fit*). Won’t this be tantamount to requiring authors support their research in a way that is either (actually) invalid, or has little to do with the error statistical properties that were actually reported and on which the acceptance was based?[ii] Continue reading

Categories: P-values, reforming the reformers, Statistics | 72 Comments

“Probabilism as an Obstacle to Statistical Fraud-Busting”

Boston Colloquium 2013-2014

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“Is the Philosophy of Probabilism an Obstacle to Statistical Fraud Busting?” was my presentation at the 2014 Boston Colloquium for the Philosophy of Science):“Revisiting the Foundations of Statistics in the Era of Big Data: Scaling Up to Meet the Challenge.”  

 As often happens, I never put these slides into a stand alone paper. But I have incorporated them into my book (in progress*), “How to Tell What’s True About Statistical Inference”. Background and slides were posted last year.

Slides (draft from Feb 21, 2014) 

Download the 54th Annual Program

Cosponsored by the Department of Mathematics & Statistics at Boston University.

Friday, February 21, 2014
10 a.m. – 5:30 p.m.
Photonics Center, 9th Floor Colloquium Room (Rm 906)
8 St. Mary’s Street

*Seeing a light at the end of tunnel, finally.
Categories: P-values, significance tests, Statistical fraudbusting, Statistics | 7 Comments

Big Data Is The New Phrenology?

Mayo:

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It happens I’ve been reading a lot lately about the assumption in social psychology and psychology in general that what they’re studying is measurable, quantifiable. Addressing the problem has been shelved to the back burner for decades thanks to some redefinitions of what it is to “measure” in psych (anything for which there’s a rule to pop out a number says Stevens–an operationalist in the naive positivist spirit). This at any rate is what I’m reading, thanks to papers sent by a colleague of Meehl’s (N. Waller).  (Here’s one by Mitchell.) I think it’s time to reopen the question.The measures I see of “severity of moral judgment”, “degree of self-esteem” and much else in psychology appear to fall into this behavior in a very non-self critical manner. No statistical window-dressing (nor banning of statistical inference) can help them become more scientific. So when I saw this on Math Babe’s twitter I decided to try the “reblog” function and see what happened. Here it is (with her F word included). The article to which she alludes is “Recruiting Better Talent Through Brain Games” )

Originally posted on mathbabe:

Have you ever heard of phrenology? It was, once upon a time, the “science” of measuring someone’s skull to understand their intellectual capabilities.

This sounds totally idiotic but was a huge fucking deal in the mid-1800’s, and really didn’t stop getting some credit until much later. I know that because I happen to own the 1911 edition of the Encyclopedia Britannica, which was written by the top scholars of the time but is now horribly and fascinatingly outdated.

For example, the entry for “Negro” is famously racist. Wikipedia has an excerpt: “Mentally the negro is inferior to the white… the arrest or even deterioration of mental development [after adolescence] is no doubt very largely due to the fact that after puberty sexual matters take the first place in the negro’s life and thoughts.”

But really that one line doesn’t tell the whole story. Here’s the whole thing…

View original 351 more words

Categories: msc kvetch, scientism, Statistics | 3 Comments

3 YEARS AGO: (FEBRUARY 2012) MEMORY LANE

3 years ago...

3 years ago…

MONTHLY MEMORY LANE: 3 years ago: February 2012. I am to mark in red three posts (or units) that seem most apt for general background on key issues in this blog. Given our Fisher reblogs, we’ve already seen many this month. So, I’m marking in red (1) The Triad, and (2) the Unit on Spanos’ misspecification tests. Plase see those posts for their discussion. The two posts from 2/8 are apt if you are interested in a famous case involving statistics at the Supreme Court. Beyond that it’s just my funny theatre of the absurd piece with Barnard. (Gelman’s is just a link to his blog.)

 

February 2012

TRIAD:

  • (2/11) R.A. Fisher: Statistical Methods and Scientific Inference
  • (2/11)  JERZY NEYMAN: Note on an Article by Sir Ronald Fisher
  • (2/12) E.S. Pearson: Statistical Concepts in Their Relation to Reality

REBLOGGED LAST WEEK

 

M-S TESTING UNIT

 

This new, once-a-month, feature began at the blog’s 3-year anniversary in Sept, 2014.

Previous 3 YEAR MEMORY LANES:

Jan. 2012

Dec. 2011

Nov. 2011

Oct. 2011

Sept. 2011 (Within “All She Wrote (so far))

Categories: 3-year memory lane, Statistics | 1 Comment

Sir Harold Jeffreys’ (tail area) one-liner: Saturday night comedy (b)

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This headliner appeared before, but to a sparse audience, so Management’s giving him another chance… His joke relates to both Senn’s post (about alternatives), and to my recent post about using (1 – β)/α as a likelihood ratio--but for very different reasons. (I’ve explained at the bottom of this “(b) draft”.)

 ….If you look closely, you’ll see that it’s actually not Jay Leno who is standing up there at the mike, (especially as he’s no longer doing the Tonight Show) ….

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It’s Sir Harold Jeffreys himself! And his (very famous) joke, I admit, is funny. So, since it’s Saturday night, let’s listen in on Sir Harold’s howler joke* in criticizing the use of p-values.

“Did you hear the one about significance testers rejecting H0 because of outcomes H0 didn’t predict?

‘What’s unusual about that?’ you ask?

What’s unusual is that they do it when these unpredicted outcomes haven’t even occurred!”

Much laughter.

[The actual quote from Jeffreys: Using p-values implies that “An hypothesis that may be true is rejected because it has failed to predict observable results that have not occurred. This seems a remarkable procedure.” (Jeffreys 1939, 316)]

I say it’s funny, so to see why I’ll strive to give it a generous interpretation. Continue reading

Categories: Comedy, Discussion continued, Fisher, Jeffreys, P-values, Statistics, Stephen Senn | 5 Comments

Stephen Senn: Fisher’s Alternative to the Alternative

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As part of the week of recognizing R.A.Fisher (February 17, 1890 – July 29, 1962), I reblog Senn from 3 years ago.  

‘Fisher’s alternative to the alternative’

By: Stephen Senn

[2012 marked] the 50th anniversary of RA Fisher’s death. It is a good excuse, I think, to draw attention to an aspect of his philosophy of significance testing. In his extremely interesting essay on Fisher, Jimmie Savage drew attention to a problem in Fisher’s approach to testing. In describing Fisher’s aversion to power functions Savage writes, ‘Fisher says that some tests are more sensitive than others, and I cannot help suspecting that that comes to very much the same thing as thinking about the power function.’ (Savage 1976) (P473).

The modern statistician, however, has an advantage here denied to Savage. Savage’s essay was published posthumously in 1976 and the lecture on which it was based was given in Detroit on 29 December 1971 (P441). At that time Fisher’s scientific correspondence did not form part of his available oeuvre but in 1990 Henry Bennett’s magnificent edition of Fisher’s statistical correspondence (Bennett 1990) was published and this throws light on many aspects of Fisher’s thought including on significance tests.

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The key letter here is Fisher’s reply of 6 October 1938 to Chester Bliss’s letter of 13 September. Bliss himself had reported an issue that had been raised with him by Snedecor on 6 September. Snedecor had pointed out that an analysis using inverse sine transformations of some data that Bliss had worked on gave a different result to an analysis of the original values. Bliss had defended his (transformed) analysis on the grounds that a) if a transformation always gave the same result as an analysis of the original data there would be no point and b) an analysis on inverse sines was a sort of weighted analysis of percentages with the transformation more appropriately reflecting the weight of information in each sample. Bliss wanted to know what Fisher thought of his reply.

Fisher replies with a ‘shorter catechism’ on transformations which ends as follows: Continue reading

Categories: Fisher, Statistics, Stephen Senn | Tags: , , , | 59 Comments

R. A. Fisher: How an Outsider Revolutionized Statistics (Aris Spanos)

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In recognition of R.A. Fisher’s birthday….

‘R. A. Fisher: How an Outsider Revolutionized Statistics’

by Aris Spanos

Few statisticians will dispute that R. A. Fisher (February 17, 1890 – July 29, 1962) is the father of modern statistics; see Savage (1976), Rao (1992). Inspired by William Gosset’s (1908) paper on the Student’s t finite sampling distribution, he recast statistics into the modern model-based induction in a series of papers in the early 1920s. He put forward a theory of optimal estimation based on the method of maximum likelihood that has changed only marginally over the last century. His significance testing, spearheaded by the p-value, provided the basis for the Neyman-Pearson theory of optimal testing in the early 1930s. According to Hald (1998)

“Fisher was a genius who almost single-handedly created the foundations for modern statistical science, without detailed study of his predecessors. When young he was ignorant not only of the Continental contributions but even of contemporary publications in English.” (p. 738)

What is not so well known is that Fisher was the ultimate outsider when he brought about this change of paradigms in statistical science. As an undergraduate, he studied mathematics at Cambridge, and then did graduate work in statistical mechanics and quantum theory. His meager knowledge of statistics came from his study of astronomy; see Box (1978). That, however did not stop him from publishing his first paper in statistics in 1912 (still an undergraduate) on “curve fitting”, questioning Karl Pearson’s method of moments and proposing a new method that was eventually to become the likelihood method in his 1921 paper. Continue reading

Categories: Fisher, phil/history of stat, Spanos, Statistics | 6 Comments

R.A. Fisher: ‘Two New Properties of Mathematical Likelihood': Just before breaking up (with N-P)

17 February 1890–29 July 1962

In recognition of R.A. Fisher’s birthday tomorrow, I will post several entries on him. I find this (1934) paper to be intriguing –immediately before the conflicts with Neyman and Pearson erupted. It represents essentially the last time he could take their work at face value, without the professional animosities that almost entirely caused, rather than being caused by, the apparent philosophical disagreements and name-calling everyone focuses on. Fisher links his tests and sufficiency, to the Neyman and Pearson lemma in terms of power.  It’s as if we may see them as ending up in a very similar place (no pun intended) while starting from different origins. I quote just the most relevant portions…the full article is linked below. I’d blogged it earlier here.  You may find some gems in it.

‘Two new Properties of Mathematical Likelihood’

by R.A. Fisher, F.R.S.

Proceedings of the Royal Society, Series A, 144: 285-307 (1934)

  The property that where a sufficient statistic exists, the likelihood, apart from a factor independent of the parameter to be estimated, is a function only of the parameter and the sufficient statistic, explains the principle result obtained by Neyman and Pearson in discussing the efficacy of tests of significance.  Neyman and Pearson introduce the notion that any chosen test of a hypothesis H0 is more powerful than any other equivalent test, with regard to an alternative hypothesis H1, when it rejects H0 in a set of samples having an assigned aggregate frequency ε when H0 is true, and the greatest possible aggregate frequency when H1 is true.

If any group of samples can be found within the region of rejection whose probability of occurrence on the hypothesis H1 is less than that of any other group of samples outside the region, but is not less on the hypothesis H0, then the test can evidently be made more powerful by substituting the one group for the other. Continue reading

Categories: Fisher, phil/history of stat, Statistics | Tags: , , , | 3 Comments

Continuing the discussion on truncation, Bayesian convergence and testing of priors

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My post “What’s wrong with taking (1 – β)/α, as a likelihood ratio comparing H0 and H1?” gave rise to a set of comments that were mostly off topic but interesting in their own right. Being too long to follow, I put what appears to be the last group of comments here, starting with Matloff’s query. Please feel free to continue the discussion here; we may want to come back to the topic. Feb 17: Please note one additional voice at the end. (Check back to that post if you want to see the history)

matloff

I see the conversation is continuing. I have not had time to follow it, but I do have a related question, on which I’d be curious as to the response of the Bayesians in our midst here.

Say the analyst is sure that μ > c, and chooses a prior distribution with support on (c,∞). That guarantees that the resulting estimate is > c. But suppose the analyst is wrong, and μ is actually less than c. (I believe that some here conceded this could happen in some cases in whcih the analyst is “sure” μ > c.) Doesn’t this violate one of the most cherished (by Bayesians) features of the Bayesian method — that the effect of the prior washes out as the sample size n goes to infinity?

Alan

(to Matloff),

The short answer is that assuming information such as “mu is greater than c” which isn’t true screws up the analysis. It’s like a mathematician starting a proof of by saying “assume 3 is an even number”. If it were possible to consistently get good results from false assumptions, there would be no need to ever get our assumptions right. Continue reading

Categories: Discussion continued, Statistics | 60 Comments

Induction, Popper and Pseudoscience

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February is a good time to read or reread these pages from Popper’s Conjectures and Refutations. Below are (a) some of my newer reflections on Popper after rereading him in the graduate seminar I taught one year ago with Aris Spanos (Phil 6334), and (b) my slides on Popper and the philosophical problem of induction, first posted here. I welcome reader questions on either.

As is typical in rereading any deep philosopher, I discover (or rediscover) different morsels of clues to understanding—whether fully intended by the philosopher or a byproduct of their other insights, and a more contemporary reading. So it is with Popper. A couple of key ideas to emerge from the seminar discussion (my slides are below) are:

  1. Unlike the “naïve” empiricists of the day, Popper recognized that observations are not just given unproblematically, but also require an interpretation, an interest, a point of view, a problem. What came first, a hypothesis or an observation? Another hypothesis, if only at a lower level, says Popper.  He draws the contrast with Wittgenstein’s “verificationism”. In typical positivist style, the verificationist sees observations as the given “atoms,” and other knowledge is built up out of truth functional operations on those atoms.[1] However, scientific generalizations beyond the given observations cannot be so deduced, hence the traditional philosophical problem of induction isn’t solvable. One is left trying to build a formal “inductive logic” (generally deductive affairs, ironically) that is thought to capture intuitions about scientific inference (a largely degenerating program). The formal probabilists, as well as philosophical Bayesianism, may be seen as descendants of the logical positivists–instrumentalists, verificationists, operationalists (and the corresponding “isms”). So understanding Popper throws a great deal of light on current day philosophy of probability and statistics.

Continue reading

Categories: Phil 6334 class material, Popper, Statistics | 7 Comments

What’s wrong with taking (1 – β)/α, as a likelihood ratio comparing H0 and H1?

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Here’s a quick note on something that I often find in discussions on tests, even though it treats “power”, which is a capacity-of-test notion, as if it were a fit-with-data notion…..

1. Take a one-sided Normal test T+: with n iid samples:

H0: µ ≤  0 against H1: µ >  0

σ = 10,  n = 100,  σ/√n =σx= 1,  α = .025.

So the test would reject H0 iff Z > c.025 =1.96. (1.96. is the “cut-off”.)

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  1. Simple rules for alternatives against which T+ has high power:
  • If we add σx (here 1) to the cut-off (here, 1.96) we are at an alternative value for µ that test T+ has .84 power to detect.
  • If we add 3σto the cut-off we are at an alternative value for µ that test T+ has ~ .999 power to detect. This value, which we can write as µ.999 = 4.96

Let the observed outcome just reach the cut-off to reject the null,z= 1.96.

If we were to form a “likelihood ratio” of μ = 4.96 compared to μ0 = 0 using

[Power(T+, 4.96)]/α,

it would be 40.  (.999/.025).

It is absurd to say the alternative 4.96 is supported 40 times as much as the null, understanding support as likelihood or comparative likelihood. (The data 1.96 are even closer to 0 than to 4.96). The same point can be made with less extreme cases.) What is commonly done next is to assign priors of .5 to the two hypotheses, yielding

Pr(H0 |z0) = 1/ (1 + 40) = .024, so Pr(H1 |z0) = .976.

Such an inference is highly unwarranted and would almost always be wrong. Continue reading

Categories: Bayesian/frequentist, law of likelihood, Statistical power, statistical tests, Statistics, Stephen Senn | 87 Comments

Stephen Senn: Is Pooling Fooling? (Guest Post)

Stephen Senn

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Stephen Senn
Head, Methodology and Statistics Group,
Competence Center for Methodology and Statistics (CCMS), Luxembourg

Is Pooling Fooling?

‘And take the case of a man who is ill. I call two physicians: they differ in opinion. I am not to lie down, and die between them: I must do something.’ Samuel Johnson, in Boswell’s A Journal of a Tour to the Hebrides

A common dilemma facing meta-analysts is what to put together with what? One may have a set of trials that seem to be approximately addressing the same question but some features may differ. For example, the inclusion criteria might have differed with some trials only admitting patients who were extremely ill but with other trials treating the moderately ill as well. Or it might be the case that different measurements have been taken in different trials. An even more extreme case occurs when different, if presumed similar, treatments have been used.

It is helpful to make a point of terminology here. In what follows I shall be talking about pooling results from various trials. This does not involve naïve pooling of patients across trials. I assume that each trial will provide a valid within- trial comparison of treatments. It is these comparisons that are to be pooled (appropriately).

A possible way to think of this is in terms of a Bayesian model with a prior distribution covering the extent to which results might differ as features of trials are changed. I don’t deny that this is sometimes an interesting way of looking at things (although I do maintain that it is much more tricky than many might suppose[1]) but I would also like to draw attention to the fact that there is a frequentist way of looking at this problem that is also useful.

Suppose that we have k ‘null’ hypotheses that we are interested in testing, each being capable of being tested in one of k trials. We can label these Hn1, Hn2, … Hnk. We are perfectly entitled to test the null hypothesis Hjoint that they are all jointly true. In doing this we can use appropriate judgement to construct a composite statistic based on all the trials whose distribution is known under the null. This is a justification for pooling. Continue reading

Categories: evidence-based policy, PhilPharma, S. Senn, Statistics | 19 Comments

2015 Saturday Night Brainstorming and Task Forces: (4th draft)

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TFSI workgroup

Saturday Night Brainstorming: The TFSI on NHST–part reblog from here and here, with a substantial 2015 update!

Each year leaders of the movement to “reform” statistical methodology in psychology, social science, and other areas of applied statistics get together around this time for a brainstorming session. They review the latest from the Task Force on Statistical Inference (TFSI), propose new regulations they would like to see adopted, not just by the APA publication manual any more, but all science journals! Since it’s Saturday night, let’s listen in on part of an (imaginary) brainstorming session of the New Reformers. 

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Frustrated that the TFSI has still not banned null hypothesis significance testing (NHST)–a fallacious version of statistical significance tests that dares to violate Fisher’s first rule: It’s illicit to move directly from statistical to substantive effects–the New Reformers have created, and very successfully published in, new meta-level research paradigms designed expressly to study (statistically!) a central question: have the carrots and sticks of reward and punishment been successful in decreasing the use of NHST, and promoting instead use of confidence intervals, power calculations, and meta-analysis of effect sizes? Or not?  

Most recently, the group has helped successfully launch a variety of “replication and reproducibility projects”. Having discovered how much the reward structure encourages bad statistics and gaming the system, they have cleverly pushed to change the reward structure: Failed replications (from a group chosen by a crowd-sourced band of replicationistas ) would not be hidden in those dusty old file drawers, but would be guaranteed to be published without that long, drawn out process of peer review. Do these failed replications indicate the original study was a false positive? or that the replication attempt is a false negative?  It’s hard to say. 

This year, as is typical, there is a new member who is pitching in to contribute what he hopes are novel ideas for reforming statistical practice. In addition, for the first time, there is a science reporter blogging the meeting for her next free lance “bad statistics” piece for a high impact science journal. Notice, it seems this committee only grows, no one has dropped off, in the 3 years I’ve followed them. 

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Pawl: This meeting will come to order. I am pleased to welcome our new member, Dr. Ian Nydes, adding to the medical strength we have recently built with epidemiologist S.C.. In addition, we have a science writer with us today, Jenina Oozo. To familiarize everyone, we begin with a review of old business, and gradually turn to new business.

Franz: It’s so darn frustrating after all these years to see researchers still using NHST methods; some of the newer modeling techniques routinely build on numerous applications of those pesky tests.

Jake: And the premier publication outlets in the social sciences still haven’t mandated the severe reforms sorely needed. Hopefully the new blood, Dr. Ian Nydes, can help us go beyond resurrecting the failed attempts of the past. Continue reading

Categories: Comedy, reforming the reformers, science communication, Statistical fraudbusting, statistical tests, Statistics | Tags: , , , , , , | 19 Comments

3 YEARS AGO: (JANUARY 2012) MEMORY LANE

3 years ago...

3 years ago…

MONTHLY MEMORY LANE: 3 years ago: January 2012. I mark in red three posts that seem most apt for general background on key issues in this blog.

January 2012

This new, once-a-month, feature began at the blog’s 3-year anniversary in Sept, 2014. I will count U-Phil’s on a single paper as one of the three I highlight (else I’d have to choose between them). I will comment on  3-year old posts from time to time.

This Memory Lane needs a bit of explanation. This blog began largely as a forum to discuss a set of contributions from a conference I organized (with A. Spanos and J. Miller*) “Statistical Science and Philosophy of Science: Where Do (Should) They meet?”at the London School of Economics, Center for the Philosophy of Natural and Social Science, CPNSS, in June 2010 (where I am a visitor). Additional papers grew out of conversations initiated soon after (with Andrew Gelman and Larry Wasserman). The conference site is here.  My reflections in this general arena (Sept. 26, 2012) are here.

As articles appeared in a special topic of the on-line journal, Rationality, Markets and Morals (RMM), edited by Max Albert[i]—also a conference participant —I would announce an open invitation to readers to take a couple of weeks to write an extended comment.  Each “U-Phil”–which stands for “U philosophize”- was a contribution to this activity. I plan to go back to that exercise at some point.  Generally I would give a “deconstruction” of the paper first, followed by U-Phils, and then the author gave responses to U-Phils and me as they wished. You can readily search this blog for all the U-Phils and deconstructions**.

I was also keeping a list of issues that we either haven’t taken up, or need to return to. One example here is: Bayesian updating and down dating. Further notes about the origins of this blog are here. I recommend everyone reread Senn’s paper.** 

For newcomers, here’s your chance to catch-up; for old timers,this is philosophy: rereading is essential!

[i] Along with Hartmut Kliemt and Bernd Lahno.

*For a full list of collaborators, sponsors, logisticians, and related collaborations, see the conference page. The full list of speakers is found there as well.

**The U-Phil exchange between Mayo and Senn was published in the same special topic of RIMM. But I still wish to know how we can cultivate “Senn’s-ability.” We could continue that activity as well, perhaps.

Previous 3 YEAR MEMORY LANES:

Dec. 2011
Nov. 2011
Oct. 2011
Sept. 2011 (Within “All She Wrote (so far))

Categories: 3-year memory lane, blog contents, Statistics, Stephen Senn, U-Phil | 2 Comments

Trial on Anil Potti’s (clinical) Trial Scandal Postponed Because Lawyers Get the Sniffles (updated)

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Trial in Medical Research Scandal Postponed
By Jay Price

DURHAM, N.C. — A judge in Durham County Superior Court has postponed the first civil trial against Duke University by the estate of a patient who had enrolled in one of a trio of clinical cancer studies that were based on bogus science.

The case is part of what the investigative TV news show “60 Minutes” said could go down in history as one of the biggest medical research frauds ever.

The trial had been scheduled to start Monday, but several attorneys involved contracted flu. Judge Robert C. Ervin hasn’t settled on a new start date, but after a conference call with him Monday night, attorneys in the case said it could be as late as this fall.

Flu? Don’t these lawyers get flu shots? Wasn’t Duke working on a flu vaccine? Delaying til Fall 2015?

The postponement delayed resolution in the long-running case for the two patients still alive among the eight who filed suit. It also prolonged a lengthy public relations headache for Duke Medicine that has included retraction of research papers in major scientific journals, the embarrassing segment on “60 Minutes” and the revelation that the lead scientist had falsely claimed to be a Rhodes Scholar in grant applications and credentials.

Because it’s not considered a class action, the eight cases may be tried individually. The one designated to come first was brought by Walter Jacobs, whose wife, Julie, had enrolled in an advanced stage lung cancer study based on the bad research. She died in 2010.

“We regret that our trial couldn’t go forward on the scheduled date,” said Raleigh attorney Thomas Henson, who is representing Jacobs. “As our filed complaint shows, this case goes straight to the basic rights of human research subjects in clinical trials, and we look forward to having those issues at the forefront of the discussion when we are able to have our trial rescheduled.”

It all began in 2006 with research led by a young Duke researcher named Anil Potti. He claimed to have found genetic markers in tumors that could predict which cancer patients might respond well to what form of cancer therapy. The discovery, which one senior Duke administrator later said would have been a sort of Holy Grail of cancer research if it had been accurate, electrified other scientists in the field.

Then, starting in 2007, came the three clinical trials aimed at testing the approach. These enrolled more than 100 lung and breast cancer patients, and were eventually expected to enroll hundreds more.

Duke shut them down permanently in 2010 after finding serious problems with Potti’s science.

Now some of the patients – or their estates, since many have died from their illnesses – are suing Duke, Potti, his mentor and research collaborator Dr. Joseph Nevins, and various Duke administrators. The suit alleges, among other things, that they had engaged in a systematic plan to commercially develop cancer tests worth billions of dollars while using science that they knew or should have known to be fraudulent.

The latest revelation in the case, based on documents that emerged from the lawsuit and first reported in the Cancer Letter, a newsletter that covers cancer research issues, is that a young researcher working with Potti had alerted university officials to problems with the research data two years before the experiments on the cancer patients were stopped. Continue reading

Categories: junk science, rejected post, Statistics | Tags: | 6 Comments

What do these share in common: m&ms, limbo stick, ovulation, Dale Carnegie? Sat night potpourri

images-2

For entertainment only

Here’s the follow-up to my last (reblogged) post. initially here. My take hasn’t changed much from 2013. Should we be labeling some pursuits “for entertainment only”? Why not? (See also a later post on the replication crisis in psych.)

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I had said I would label as pseudoscience or questionable science any enterprise that regularly permits the kind of ‘verification biases’ in the statistical dirty laundry list.  How regularly? (I’ve been asked)

Well, surely if it’s as regular as, say, much of social psychology, it goes over the line. But it’s not mere regularity, it’s the nature of the data, the type of inferences being drawn, and the extent of self-scrutiny and recognition of errors shown (or not shown). The regularity is just a consequence of the methodological holes. My standards may be considerably more stringent than most, but quite aside from statistical issues, I simply do not find hypotheses well-tested if they are based on “experiments” that consist of giving questionnaires. At least not without a lot more self-scrutiny and discussion of flaws than I ever see. (There may be counterexamples.)

Attempts to recreate phenomena of interest in typical social science “labs” leave me with the same doubts. Huge gaps often exist between elicited and inferred results. One might locate the problem under “external validity” but to me it is just the general problem of relating statistical data to substantive claims.

Experimental economists (expereconomists) take lab results plus statistics to warrant sometimes ingenious inferences about substantive hypotheses.  Vernon Smith (of the Nobel Prize in Econ) is rare in subjecting his own results to “stress tests”.  I’m not withdrawing the optimistic assertions he cites from EGEK (Mayo 1996) on Duhem-Quine (e.g., from “Rhetoric and Reality” 2001, p. 29). I’d still maintain, “Literal control is not needed to attribute experimental results correctly (whether to affirm or deny a hypothesis). Enough experimental knowledge will do”.  But that requires piece-meal strategies that accumulate, and at least a little bit of “theory” and/or a decent amount of causal understanding.[1]

I think the generalizations extracted from questionnaires allow for an enormous amount of “reading into” the data. Suddenly one finds the “best” explanation. Questionnaires should be deconstructed for how they may be misinterpreted, not to mention how responders tend to guess what the experimenter is looking for. (I’m reminded of the current hoopla over questionnaires on breadwinners, housework and divorce rates!) I respond with the same eye-rolling to just-so story telling along the lines of evolutionary psychology.

I apply the “Stapel test”: Even if Stapel had bothered to actually carry out the data-collection plans that he so carefully crafted, I would not find the inferences especially telling in the least. Take for example the planned-but-not-implemented study discussed in the recent New York Times article on Stapel: Continue reading

Categories: junk science, Statistical fraudbusting, Statistics | 3 Comments

Some statistical dirty laundry

Objectivity 1: Will the Real Junk Science Please Stand Up?

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It’s an apt time to reblog the “statistical dirty laundry” post from 2013 here. I hope we can take up the recommendations from Simmons, Nelson and Simonsohn at the end (Note [5]), which we didn’t last time around.

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I finally had a chance to fully read the 2012 Tilberg Report* on “Flawed Science” last night. Here are some stray thoughts…

1. Slipping into pseudoscience.
The authors of the Report say they never anticipated giving a laundry list of “undesirable conduct” by which researchers can flout pretty obvious requirements for the responsible practice of science. It was an accidental byproduct of the investigation of one case (Diederik Stapel, social psychology) that they walked into a culture of “verification bias”[1]. Maybe that’s why I find it so telling. It’s as if they could scarcely believe their ears when people they interviewed “defended the serious and less serious violations of proper scientific method with the words: that is what I have learned in practice; everyone in my research environment does the same, and so does everyone we talk to at international conferences” (Report 48). So they trot out some obvious rules, and it seems to me that they do a rather good job:

One of the most fundamental rules of scientific research is that an investigation must be designed in such a way that facts that might refute the research hypotheses are given at least an equal chance of emerging as do facts that confirm the research hypotheses. Violations of this fundamental rule, such as continuing an experiment until it works as desired, or excluding unwelcome experimental subjects or results, inevitably tends to confirm the researcher’s research hypotheses, and essentially render the hypotheses immune to the facts…. [T]he use of research procedures in such a way as to ‘repress’ negative results by some means” may be called verification bias. [my emphasis] (Report, 48).

I would place techniques for ‘verification bias’ under the general umbrella of techniques for squelching stringent criticism and repressing severe tests. These gambits make it so easy to find apparent support for one’s pet theory or hypotheses, as to count as no evidence at all (see some from their list ). Any field that regularly proceeds this way I would call a pseudoscience, or non-science, following Popper. “Observations or experiments can be accepted as supporting a theory (or a hypothesis, or a scientific assertion) only if these observations or experiments are severe tests of the theory” (Popper 1994, p. 89). [2] It is unclear at what point a field slips into the pseudoscience realm.

2. A role for philosophy of science?
I am intrigued that one of the final recommendations in the Report is this: Continue reading

Categories: junk science, reproducibility, spurious p values, Statistics | 27 Comments

Power Analysis and Non-Replicability: If bad statistics is prevalent in your field, does it follow you can’t be guilty of scientific fraud?

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fraudbusters

If questionable research practices (QRPs) are prevalent in your field, then apparently you can’t be guilty of scientific misconduct or fraud (by mere QRP finagling), or so some suggest. Isn’t that an incentive for making QRPs the norm? 

The following is a recent blog discussion (by  Ulrich Schimmack) on the Jens Förster scandal: I thank Richard Gill for alerting me. I haven’t fully analyzed Schimmack’s arguments, so please share your reactions. I agree with him on the importance of power analysis, but I’m not sure that the way he’s using it (via his “R index”) shows what he claims. Nor do I see how any of this invalidates, or spares Förster from, the fraud allegations along the lines of Simonsohn[i]. Most importantly, I don’t see that cheating one way vs another changes the scientific status of Forster’s flawed inference. Forster already admitted that faced with unfavorable results, he’d always find ways to fix things until he got results in sync with his theory (on the social psychology of creativity priming). Fraud by any other name.

Förster

Förster

The official report, “Suspicion of scientific misconduct by Dr. Jens Förster,” is anonymous and dated September 2012. An earlier post on this blog, “Who ya gonna call for statistical fraud busting” featured a discussion by Neuroskeptic that I found illuminating, from Discover Magazine: On the “Suspicion of Scientific Misconduct by Jens Förster. Also see Retraction Watch.

Does anyone know the official status of the Forster case?

How Power Analysis Could Have Prevented the Sad Story of Dr. Förster”

From Ulrich Schimmack’s “Replicability Index” blog January 2, 2015. A January 14, 2015 update is here. (occasional emphasis in bright red is mine) Continue reading

Categories: junk science, reproducibility, Statistical fraudbusting, Statistical power, Statistics | Tags: | 22 Comments

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