[silk] Fwd: UK judge bans Bayesian logic
Sorry for breaking this thread, sending along a couple of emails forwarded on with permission from another list where this was being discussed. Email #1 below --srs(iPad) Begin forwarded message: - Forwarded by Suresh Ramasubramanian1/India/IBM on 10/04/2011 07:47 AM - From:Joe St Sauver j...@oregon.uoregon.edu To:Suresh Ramasubramanian1/India/IBM@IBMIN, Date:10/03/2011 06:53 PM Subject:Re: Fw: [silk] UK judge bans Bayesian logic Hi Suresh, Thanks for passing along the pointer to the Guardian article # http://www.guardian.co.uk/law/2011/oct/02/formula-justice-bayes-theorem-miscarriage The funny thing is that the bench in this case has actually weighed in, perhaps unintentionally, on a long-standing debate in the statistical community -- folks may or may not know this, but there are different camps in the statistical community, much as there often is in many different academic disciplines. For example, in the Decision Sciences Department at UO, the chair (and my dissertation advisor) was an adherent of Fisher; another faculty member, very well regarded, was a passionate and well published advocate of Bayes. If folks are curious what all the fuss is about, I'd recommend the article, Why Isn't Everyone a Bayesian? by B. Efron, from the February 1986 American Statistician. A copy of that article is available online at http://www.isye.gatech.edu/~brani/isyebayes/bank/EfronWhyEveryone.pdf If that article is too opaque, and you'd just like to see if you yourself are a latent Bayesian, consider the classic Monty Hall game show -- for those of you who might never have seen it, Monty would select a member of the audience and offer them the opportunity to pick one of three doors. Behind one door, there might be a terrific prize, such as a new car. Behind another door, there might be a gag prize such as a lifesize crude ceramic billy goat, the perfect kitsch addition for your living room, eh? And then, behind the third door, there's some other prize, which might be pretty cool, or pretty lame, it would vary, but usually be something like a major appliance. Then again, sometimes Monty might have two lame prizes. The contestant gets to pick one door. At that point, what is his or her chance of winning the high dollar value good prize, e.g., the car? (most folks would say, 1-in-3) To make things more interesting, Monty would remind the contestant that while there's a terrific prize behind one of the doors, they might not have picked it. He'd then offer them cash-in-hand, if they want to take the money and run. To help the contestant, Monty would also open one door. Since Monty knew what door actually has the top prize, he'd never open that one. You might see, instead, a nice washer and dryer set, or maybe the goat. You'd never see the car (if there was a car). And now we come to the question that determines if you're a Fisherian or a Bayesian at heart: *what's the probability that the contestant will win the car NOW that Monty has opened one door?* Remember, there are two choices left, one of which has the car, one of which does not. Fisher and his fans would say, obviously, 1-in-2, or 50%. Bayes and his adherents would say, no, the correct answer is 2-in-3, or 66%. If you find yourself leaning toward Bayes, let me ask you an additional question: assume the audience member is given the chance to *switch* their choice, and pick the other unopened door. Should they? Would it matter? If Fisher is right, both doors have an equal chance of being right, and there's no reason why the person should switch. What would Bayesians say? :-; http://en.wikipedia.org/wiki/Monty_Hall_problem#Bayes.27_theorem Regards, Joe
[silk] Fwd: UK judge bans Bayesian logic
--srs(iPad) Begin forwarded message: - Forwarded by Suresh Ramasubramanian1/India/IBM on 10/04/2011 07:48 AM - From:Dave CROCKER dcroc...@bbiw.net To:Joe St Sauver j...@oregon.uoregon.edu, Cc:Suresh Ramasubramanian1/India/IBM@IBMIN Date:10/03/2011 08:18 PM Subject:Re: Fw: [silk] UK judge bans Bayesian logic On 10/3/2011 5:26 AM, Joe St Sauver wrote: If that article is too opaque, and you'd just like to see if you yourself are a latent Bayesian, consider the classic Monty Hall game show -- for those of you who might never have seen it, Monty would select a member of the audience and offer them the opportunity to pick one of three doors. I've long held two views that diverge from much of what is used for behavior-related research: 1. Sophisticated statistics are appropriate only when there is massively good data that is extremely well understood. Since that's rare, most use of statistics should be simple and obvious and use algorithms that are relatively IN sensitive. 2. The framework or methodology for approaching an analysis is far more important than the statistical algorithm. For example, from the Guardian article: When Sally Clark was convicted in 1999 of smothering her two children, jurors and judges bought into the claim that the odds of siblings dying by cot death was too unlikely for her to be innocent. In fact, it was statistically more rare for a mother to kill both her children. That's highlights a methodology error in the original work and it's one that is fundamental. The original trial took a statistic in isolation rather than asking about comparable choices and /their/ numbers. (One of the engineers who worked on the original HP hand caclulator in the early 70s wrote an article about its impact. He cited an experience with a banker, when he and some friends were trying to get a loan for an airplane purchase and they haggled with the banker over some of the numbers. The engineer pulled out his brand new (and extremely rare) calculator, pushed a few buttons, showed the result to the banker and the banker caved on the negotiation, without question any of the underlying details.) For most behavioral analysis, we simply do not know enough about the surrounding environment or the population to be as precise as many statistics tools imply. And too frequently that surrounding analytic framework has a deep flaw that isn't even within site of those deciding whether to accept the statistical numbers. Two anecdotes in this vein... Back when I was still in school mode, I twice got into quite a bit of trouble for my simplistic attitude. Just after dropping out of undergrad, I interviewed with the folks at Engelbart's SRI project, for a kind of user support job. (These are the folks that invented the mouse, office automatic, and otherwise laid the foundation for the work that was done at Xerox Parc and then Apple.) I had been dealing with them for a couple of years, so this was a friendly interview, until... at lunch with the guy I knew, and the guy who worked for him who would be my boss, the latter described the challenges of developing a good survey instrument to assess user 'needs'. In a fit of massive political stupidity, I noted that I had been told that such things were indeed hard to do well but that in the interim, couldn't he just /ask/ users what they wanted? He immediately stiffened and -- I swear he started looking down his nose at me -- he said that that would be methodologically naive. I looked at his boss who shrugged with an obvious meaning that this meant he knew the guy would not tolerate my working for him. We were done. On the other hand, it was my first taste of Anchor Steam Beer. And then when working at Rand, there was some spectacularly good information processing / cognitive psychology work being done by 3 very hot researchers. (The term cognitive psych was not yet in vogue for info proc work; these guys were trailblazers on the psych side and were /very/ well regarded in the field with an impressive publication record.) To get a raise at Rand, you needed to publish Rand Reports, no matter what outside publications you had. So they assembled their hottest published papers into a compendium. Rand Reports are refereed and they asked me to be a reviewer. There were few folk at Rand with a psych and computer science background, especially with any background on info processing psych. Unfortunately I was back in school by then and taking a multivarate stat course an dthe prof had just made us do an 'error' paper, where the term was not about the error part of stats algorithms but about methodological errors. In assigning the task -- we had to find an example in the literature of our field, in my case that was Human/Mass
