Getting Credit (or blame) for Something You Didn’t Do (BP oil spill)

Posted on April 19, 2014 by Mayo

 

Spill Cam

Spill Cam

Four years ago, many of us were glued to the “spill cam” showing, in real time, the gushing oil from the April 20, 2010 explosion sinking the Deepwater Horizon oil rig in the Gulf of Mexico, killing 11, and spewing oil until July 15 (see video clip that was added below).Remember junk shots, top kill, blowout preventers? [1] The EPA has lifted its gulf drilling ban on BP just a couple of weeks ago* (BP has paid around $13 $27 billion in fines and compensation), and April 20, 2014, is the deadline to properly file forms for new compensations.

(*After which BP had another small spill in Lake Michigan.)

But what happened to the 200 million gallons of oil? Has it vanished or just sunk to the bottom of the sea by dispersants which may have caused hidden destruction of sea life? I don’t know, but given it’s Saturday night, let’s listen in to a reblog of a spill-related variation on the second of two original “overheard at the comedy hour” jokes. 

Categories: Comedy, Statistics | 8 Comments

Duality: Confidence intervals and the severity of tests

Posted on April 17, 2014 by Mayo

confidence intervalA question came up in our seminar today about how to understand the duality between a simple one-sided test and a lower limit (LL) of a corresponding 1-sided confidence interval estimate. This is also a good route to SEV (i.e., severity). Here’s a quick answer: Continue reading →

Categories: confidence intervals and tests, Phil6334 | Leave a comment

A. Spanos: Jerzy Neyman and his Enduring Legacy

Posted on April 16, 2014 by Mayo

A Statistical Model as a Chance Mechanism
Aris Spanos 

Jerzy Neyman (April 16, 1894 – August 5, 1981), was a Polish/American statistician[i] who spent most of his professional career at the University of California, Berkeley. Neyman is best known in statistics for his pioneering contributions in framing the Neyman-Pearson (N-P) optimal theory of hypothesis testing and his theory of Confidence Intervals. (This article was first posted here.)

Neyman: 16 April

Neyman: 16 April 1894 – 5 Aug 1981

One of Neyman’s most remarkable, but least recognized, achievements was his adapting of Fisher’s (1922) notion of a statistical model to render it pertinent for  non-random samples. Continue reading →

Categories: phil/history of stat, Spanos, Statistics | Tags: , | 4 Comments

Phil 6334: Notes on Bayesian Inference: Day #11 Slides

Posted on April 14, 2014 by Mayo

 

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A. Spanos Probability/Statistics Lecture Notes 7: An Introduction to Bayesian Inference (4/10/14)

Categories: Bayesian/frequentist, Phil 6334 class material, Statistics | 11 Comments

“Murder or Coincidence?” Statistical Error in Court: Richard Gill (TEDx video)

Posted on April 12, 2014 by Mayo

“There was a vain and ambitious hospital director. A bad statistician. ..There were good medics and bad medics, good nurses and bad nurses, good cops and bad cops … Apparently, even some people in the Public Prosecution service found the witch hunt deeply disturbing.”

This is how Richard Gill, statistician at Leiden University, describes a feature film (Lucia de B.) just released about the case of Lucia de Berk, a nurse found guilty of several murders based largely on statistics. Gill is widely-known (among other things) for showing the flawed statistical analysis used to convict her, which ultimately led (after Gill’s tireless efforts) to her conviction being revoked. (I hope they translate the film into English.) In a recent e-mail Gill writes:

“The Dutch are going into an orgy of feel-good tear-jerking sentimentality as a movie comes out (the premiere is tonight) about the case. It will be a good movie, actually, but it only tells one side of the story. …When a jumbo jet goes down we find out what went wrong and prevent it from happening again. The Lucia case was a similar disaster. But no one even *knows* what went wrong. It can happen again tomorrow.

I spoke about it a couple of days ago at a TEDx event (Flanders).

You can find some p-values in my slides [“Murder by Numbers”, pasted below the video]. They were important – first in convicting Lucia, later in getting her a fair re-trial.”

Since it’s Saturday night, let’s watch Gill’s TEDx talk, “Statistical Error in court”.

Slides from the Talk: “Murder by Numbers”:

 

Categories: junk science, P-values, PhilStatLaw, science communication, Statistics | Tags: | Leave a comment

“Out Damned Pseudoscience: Non-significant results are the new ‘Significant’ results!” (update)

Posted on April 8, 2014 by Mayo

Sell me that antiseptic!

We were reading “Out, Damned Spot: Can the ‘Macbeth effect’ be replicated?” (Earp,B., Everett,J., Madva,E., and Hamlin,J. 2014, in Basic and Applied Social Psychology 36: 91-8) in an informal gathering of our 6334 seminar yesterday afternoon at Thebes. Some of the graduate students are interested in so-called “experimental” philosophy, and I asked for an example that used statistics for purposes of analysis. The example–and it’s a great one (thanks Rory M!)–revolves around priming research in social psychology. Yes the field that has come in for so much criticism as of late, especially after Diederik Stapel was found to have been fabricating data altogether (search this blog, e.g., here).[1] Continue reading →

Categories: fallacy of non-significance, junk science, reformers, Statistics | 15 Comments

Phil 6334: Duhem’s Problem, highly probable vs highly probed; Day #9 Slides

Posted on April 6, 2014 by Mayo

 

picture-216-1April 3, 2014: We interspersed discussion with slides; these cover the main readings of the day (check syllabus): the Duhem’s Problem and the Bayesian Way, and “Highly probable vs Highly Probed”. syllabus four. Slides are below (followers of this blog will be familiar with most of this, e.g., here). We also did further work on misspecification testing.

Monday, April 7, is an optional outing, “a seminar class trip”

"Thebes", Blacksburg, VA

“Thebes”, Blacksburg, VA

you might say, here at Thebes at which time we will analyze the statistical curves of the mountains, pie charts of pizza, and (seriously) study some experiments on the problem of replication in “the Hamlet Effect in social psychology”. If you’re around please bop in!

Mayo’s slides on Duhem’s Problem and more from April 3 (Day#9):

 

 

Categories: Bayesian/frequentist, highly probable vs highly probed, misspecification testing | 8 Comments

Who is allowed to cheat? I.J. Good and that after dinner comedy hour….

Posted on April 5, 2014 by Mayo

UnknownIt was from my Virginia Tech colleague I.J. Good (in statistics), who died five years ago (April 5, 2009), at 93, that I learned most of what I call “howlers” on this blog. His favorites were based on the “paradoxes” of stopping rules. (I had posted this last year here.)

“In conversation I have emphasized to other statisticians, starting in 1950, that, in virtue of the ‘law of the iterated logarithm,’ by optional stopping an arbitrarily high sigmage, and therefore an arbitrarily small tail-area probability, can be attained even when the null hypothesis is true. In other words if a Fisherian is prepared to use optional stopping (which usually he is not) he can be sure of rejecting a true null hypothesis provided that he is prepared to go on sampling for a long time. The way I usually express this ‘paradox’ is that a Fisherian [but not a Bayesian] can cheat by pretending he has a plane to catch like a gambler who leaves the table when he is ahead” (Good 1983, 135) [*]

Continue reading →

Categories: Bayesian/frequentist, Comedy, Statistics | Tags: , , | 18 Comments

Self-referential blogpost (conditionally accepted*)

Posted on April 3, 2014 by Mayo

This is a blogpost on a talk (by Jeremy Fox) on blogging that will be live tweeted here at Virginia Tech on Monday April 7, and the moment I post this blog on “Blogging as a Mode of Scientific Communication” it will be tweeted. Live.

Jeremy’s upcoming talk on blogging will be live-tweeted by @FisheriesBlog, 1 pm EDT Apr. 7

Posted on April 3, 2014 by Jeremy Fox

If you like to follow live tweets of talks, you’re in luck: my upcoming Virginia Tech talk on blogging will be live tweeted by Brandon Peoples, a grad student there who co-authors The Fisheries Blog. Follow @FisheriesBlog at 1 pm US Eastern Daylight Time on Monday, April 7 for the live tweets.

Jeremy Fox’s excellent blog, “Dynamic Ecology,” often discusses matters statistical from a perspective in sync with error statistics.

I’ve never been invited to talk about blogging or even to blog about blogging, maybe this is a new trend. I look forward to meeting him (live!).

va-tech-poster

* Posts that don’t directly pertain to philosophy of science/statistics are placed under “rejected posts” but since this is a metablogpost on a talk on a blog pertaining to statistics it has been “conditionally accepted”, unconditionally, i.e., without conditions.

Categories: Announcement, Metablog | Leave a comment

Skeptical and enthusiastic Bayesian priors for beliefs about insane asylum renovations at Dept of Homeland Security: I’m skeptical and unenthusiastic

Posted on April 1, 2014 by Mayo
Danver State Hospital

Danvers State Hospital

I had heard of medical designs that employ individuals who supply Bayesian subjective priors that are deemed either “enthusiastic” or “skeptical” as regards the probable value of medical treatments.[i] From what I gather, these priors are combined with data from trials in order to help decide whether to stop trials early or continue. But I’d never heard of these Bayesian designs in relation to decisions about building security or renovations! Listen to this…. Continue reading →

Categories: junk science, Statistics | 11 Comments

Phil 6334: March 26, philosophy of misspecification testing (Day #9 slides)

Posted on March 30, 2014 by Mayo

 

may-4-8-aris-spanos-e2809contology-methodology-in-statistical-modelinge2809d“Probability/Statistics Lecture Notes 6: An Introduction to Mis-Specification (M-S) Testing” (Aris Spanos)

 

[Other slides from Day 9 by guest, John Byrd, can be found here.]

Categories: misspecification testing, Phil 6334 class material, Spanos, Statistics | Leave a comment

Winner of the March 2014 palindrome contest (rejected post)

Posted on March 29, 2014 by Mayo
caitlin-parkerWinner of the March 2014 Palindrome Contest

Caitlin Parker

Palindrome: 

Able, we’d well aim on. I bet on a note. Binomial? Lewd. Ew, Elba!

The requirement was: A palindrome with Elba plus Binomial with an optional second word: bet. A palindrome that uses both Binomial and bet topped an acceptable palindrome that only uses Binomial.

Short bio: 
Caitlin Parker is a first-year master’s student in the Philosophy department at Virginia Tech. Though her interests are in philosophy of science and statistics, she also has experience doing psychological research. Continue reading →

Categories: Announcement, Palindrome, Rejected Posts | Leave a comment

Severe osteometric probing of skeletal remains: John Byrd

Posted on March 28, 2014 by Mayo

images-3John E. Byrd, Ph.D. D-ABFA

Central Identification Laboratory
JPAC

Guest, March 27, PHil 6334

“Statistical Considerations of the Histomorphometric Test Protocol for Determination of Human Origin of Skeletal Remains”

 By:
Byrd 1John E. Byrd, Ph.D. D-ABFA
Maria-Teresa Tersigni-Tarrant, Ph.D.
Central Identification Laboratory
JPAC

Categories: Phil6334, Philosophy of Statistics, Statistics | 1 Comment

Phil 6334: Misspecification Testing: Ordering From A Full Diagnostic Menu (part 1)

Posted on March 26, 2014 by Mayo

.

 We’re going to be discussing the philosophy of m-s testing today in our seminar, so I’m reblogging this from Feb. 2012. I’ve linked the 3 follow-ups below. Check the original posts for some good discussion. (Note visitor*)

“This is the kind of cure that kills the patient!”

is the line of Aris Spanos that I most remember from when I first heard him talk about testing assumptions of, and respecifying, statistical models in 1999.  (The patient, of course, is the statistical model.) On finishing my book, EGEK 1996, I had been keen to fill its central gaps one of which was fleshing out a crucial piece of the error-statistical framework of learning from error: How to validate the assumptions of statistical models. But the whole problem turned out to be far more philosophically—not to mention technically—challenging than I imagined. I will try (in 3 short posts) to sketch a procedure that I think puts the entire process of model validation on a sound logical footing. Continue reading →

Categories: Intro MS Testing, Statistics | Tags: , , , , | 16 Comments

The Unexpected Way Philosophy Majors Are Changing The World Of Business

Posted on March 25, 2014 by Mayo

 

Philosopher

Philosopher

“Philosophy majors rule” according to this recent article. We philosophers should be getting the word out. Admittedly, the type of people inclined to do well in philosophy are already likely to succeed in analytic areas. Coupled with the chuzpah of taking up an “outmoded and impractical” major like philosophy in the first place, innovative tendencies are not surprising.  But can the study of philosophy also promote these capacities? I think it can and does; yet it could be far more effective than it is, if it was less hermetic and more engaged with problem-solving across the landscape of science,statistics,law,medicine,and evidence-based policy. Here’s the article: Continue reading →

Categories: philosophy of science, Philosophy of Statistics, Statistics | 1 Comment

Fallacies of statistics & statistics journalism, and how to avoid them: Summary & Slides Day #8 (Phil 6334)

Posted on March 22, 2014 by Mayo
picture-216-1

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We spent the first half of Thursday’s seminar discussing the Fisher, Neyman, and E. Pearson “triad”[i]. So, since it’s Saturday night, join me in rereading for the nth time these three very short articles. The key issues were: error of the second kind, behavioristic vs evidential interpretations, and Fisher’s mysterious fiducial intervals. Although we often hear exaggerated accounts of the differences in the Fisherian vs Neyman-Pearson (NP) methodology, in fact, N-P were simply providing Fisher’s tests with a logical ground (even though other foundations for tests are still possible), and Fisher welcomed this gladly. Notably, with the single null hypothesis, N-P showed that it was possible to have tests where the probability of rejecting the null when true exceeded the probability of rejecting it when false. Hacking called such tests “worse than useless”, and N-P develop a theory of testing that avoids such problems. Statistical journalists who report on the alleged “inconsistent hybrid” (a term popularized by Gigerenzer) should recognize the extent to which the apparent disagreements on method reflect professional squabbling between Fisher and Neyman after 1935 [A recent example is a Nature article by R. Nuzzo in ii below]. The two types of tests are best seen as asking different questions in different contexts. They both follow error-statistical reasoning. Continue reading →

Categories: phil/history of stat, Phil6334, science communication, Severity, significance tests, Statistics | Tags: | 35 Comments

Power taboos: Statue of Liberty, Senn, Neyman, Carnap, Severity

Posted on March 19, 2014 by Mayo

Unknown-3Is it taboo to use a test’s power to assess what may be learned from the data in front of us? (Is it limited to pre-data planning?) If not entirely taboo, some regard power as irrelevant post-data[i], and the reason I’ve heard is along the lines of an analogy Stephen Senn gave today (in a comment discussing his last post here)[ii].

Senn comment: So let me give you another analogy to your (very interesting) fire alarm analogy (My analogy is imperfect but so is the fire alarm.) If you want to cross the Atlantic from Glasgow you should do some serious calculations to decide what boat you need. However, if several days later you arrive at the Statue of Liberty the fact that you see it is more important than the size of the boat for deciding that you did, indeed, cross the Atlantic.

My fire alarm analogy is here. My analogy presumes you are assessing the situation (about the fire) long distance. Continue reading →

Categories: exchange with commentators, Neyman's Nursery, P-values, Phil6334, power, Stephen Senn | 6 Comments

Stephen Senn: “Delta Force: To what extent is clinical relevance relevant?” (Guest Post)

Posted on March 17, 2014 by Mayo
Stephen Senn

Senn

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

Delta Force
To what extent is clinical relevance relevant?

Inspiration
This note has been inspired by a Twitter exchange with respected scientist and famous blogger  David Colquhoun. He queried whether a treatment that had 2/3 of an effect that would be described as clinically relevant could be useful. I was surprised at the question, since I would regard it as being pretty obvious that it could but, on reflection, I realise that things that may seem obvious to some who have worked in drug development may not be obvious to others, and if they are not obvious to others are either in need of a defence or wrong. I don’t think I am wrong and this note is to explain my thinking on the subject. Continue reading →

Categories: power, Statistics, Stephen Senn | 39 Comments

New SEV calculator (guest app: Durvasula)

Posted on March 15, 2014 by Mayo
Unknown-1Karthik Durvasula, a blog follower[i], sent me a highly apt severity app that he created: https://karthikdurvasula.shinyapps.io/Severity_Calculator/
updated June,
I have his permission to post it or use it for pedagogical purposes, so since it’s Saturday night, go ahead and have some fun with it. Durvasula had the great idea of using it to illustrate howlers. Also, I would add, to discover them.
It follows many of the elements of the Excel Sev Program discussed recently, but it’s easier to use.* (I’ll add some notes about the particular claim (i.e, discrepancy) for which SEV is being computed later on).
*If others want to tweak or improve it, he might pass on the source code (write to me on this).
[i] I might note that Durvasula was the winner of the January palindrome contest.
Categories: Severity, Statistics | 12 Comments

Get empowered to detect power howlers

Posted on March 12, 2014 by Mayo

questionmark pinkIf a test’s power to detect µ’ is low then a statistically significant result is good/lousy evidence of discrepancy µ’? Which is it?

If your smoke alarm has little capability of triggering unless your house is fully ablaze, then if it has triggered, is that a strong or weak indication of a fire? Compare this insensitive smoke alarm to one that is so sensitive that burning toast sets it off. The answer is: that the alarm from the insensitive detector is triggered is a good indication of the presence of (some) fire, while hearing the ultra sensitive alarm go off is not.[i]

Yet I often hear people say things to the effect that: Continue reading →

Categories: confidence intervals and tests, power, Statistics | 34 Comments

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