Scrap Statistics, Begin Anew

I only am escaped alone to tell thee.
I only am escaped alone to tell thee.

You or I might perhaps be excused if we sometimes toyed with solipsism, especially when we reflect on the utter failure of our writings to produce the smallest effect in the alleged external world. —David Stove, “Epistemology and the Ishmael Effect.”

Statistics is broken. When it works, it usually does so in spite of itself. When it doesn’t, which is increasingly often, it inflates egos, promulgates scientism, idolizes quantification, supports ideologies, and encourages magical thinking.

I’m not going to prove any of that today (you’re welcome to read old posts for corroboration), but assume it. This is just a Friday rant.

I weep over the difficulty of explaining things. I can’t make what is obvious to me plain to others. Flaubert was right: “Human speech is like a cracked kettle on which we tap crude rhythms for bears to dance to, while we long to make music that will melt the stars.”

So most of the fault is mine. But not all of it.

Last week I had as a header this blurb: In Nate Silver’s book The Signal and the Noise: Why So Many Predictions Fail he says (p. 68) “Recently, however, some well-respected statisticians have begun to argue that frequentist statistics should no longer be taught to undergraduates.” That footnote recommended this paper.

Easy to say. Impossible to do. You cannot, in any university I know, teach unapproved material. There are exceptions for “PhD-level” courses and the like, where the air is thin and the seats never filled, but for undergraduates you must adhere to the party line. The excuse for this is circular: students must be taught what’s approved because what’s approved is what students must be taught.

The scheme does work, however, for material which resembles cookbook recipes. Rigid syllabuses are best for welding, accountancy, physics, and sharpshooting courses. That’s why the Army uses them. But they fail miserably in what used to be called the humanities, which I say includes probability; at least its philosophical side. Humanitarians see themselves as scientists these days. Only way to get funding, I guess. Skip it.

I don’t mean to swap Bayes with frequentism, at least not in the way most people think of Bayes. Problem is everybody learns Bayes after learning frequentism, which is like a malarial infection that can’t be shaken. Frequentists love to create hypotheses? So do Bayesians. Frequentists form an unnatural and creepy fascination with parameters? So too Bayesians. Frequentists point to the occult powers of “randomization”? Bayesians nervously follow suit. Effect is that there’s very little practical difference between the two methods. (Though you wouldn’t know it listening to them bickering.)

There is no cure for malaria. Best maneuver is to avoid areas where infections are prevalent. That unfortunately means learning probability and statistics outside those departments. There’s some hope they can be learnt from certain physicists, but a weak one. The lure of quantification is strong there, and the probability is incidental.

One can always wander to the website of some eccentric—a refugee from academia—but that isn’t systematic enough for lasting consequence.

I don’t have a solution. And what am I doing wasting my time wallowing? I have to finish my book.


  1. MattS

    {insert generic melodramatic reply here}

  2. Briggs


    Somewhat of a touchy subject. I’ve received three emails from colleagues thus far (high for a Friday): two in support, one not. All anonymous.

  3. MattS

    Can you post the one not in support?

  4. Briggs


    Nope. All asked to be anonymous. But it said nothing you couldn’t imagine it saying (see the header above).

  5. Andy

    But we still love you.

    Wha book? Whens it out? What will it contain? Let us know!

  6. MattS


    I thought you might get a kick out of this. The Ig Nobel prizes are out and you might like this one:

    PROBABILITY PRIZE: Bert Tolkamp [UK, the NETHERLANDS], Marie Haskell [UK], Fritha Langford [UK, CANADA], David Roberts [UK], and Colin Morgan [UK], for making two related discoveries: First, that the longer a cow has been lying down, the more likely that cow will soon stand up; and Second, that once a cow stands up, you cannot easily predict how soon that cow will lie down again.

    REFERENCE: “Are Cows More Likely to Lie Down the Longer They Stand?” Bert J. Tolkamp, Marie J. Haskell, Fritha M. Langford, David J. Roberts, Colin A. Morgan, Applied Animal Behaviour Science, vol. 124, nos. 1-2, 2010, pp. 1–10.

    – See more at:

  7. Ray

    “There’s some hope they can be learnt from certain physicists, but a weak one.”
    You mean I should have taken that course in statistical mechanics after all?
    I took the mechanics course and we started with 36 people in class and ended with 6. The profesor tried to talk us survivors into signing up for statistical mechanics. After that attrition rate the thought of taking statistical mechanics was too terrifying.

  8. Dennis Dunton

    “. And what am I doing wasting my time wallowing? I have to finish my book.”

    Oh Idunno…a good wallow is kinda relaxing from time to time.

  9. Scotian

    Ray, a 5/6 attrition rate, I’m impressed. I presume that this was a professor new to the course who followed a predecessor who taugth a slack course. A similar thing happened to me when I took over a second year analytical mechanics course because it was filled with chemistry majors who were looking for an easy elective. Now only strong chemistry majors enrol along with the physics and math majors so that this has not happened again. By the way, statistical mechanics has very little content overlap with analytical mechanics, although that does not make it any easier.

    Ask any physics major what was his toughest course and the answer is always analytical (classical) mechanics. This is the way it is, so feel proud and realize that no other course will seem difficult by contrast. Incidentally, I teach all these courses.

  10. Ray

    The mechanics course was taught by Dr. Miller, AKA Dr. Death, one of the few professors I still remember. It really was a formidable course. Most people didn’t last the first semester. It was like playing Russian roulette with 5 rounds in the cylinder. I wasn’t the best student in class and figured my chance of surviving another round with Dr. Miller was nil. I probably should have done a Kaplan-Meier survival analysis to verify this.

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