Correlation doesn’t imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there’.

Don’t feel sad. Coz 5 out of 5 ain’t bad

May 10th, 2013 Chris Lloyd Posted in Politics, Probability, Public Interest No Comments »

The Miles Franklin literary award is an annual literary prize given to fiction describing Australian stories. It is the most prestigious award of its kind, and has grown in importance since it began in 1957.

In 2009, the short list of five was all male. This event was widely referred to as the “sausage fest.” In response to the perceived gender bias suggested by this result, the female only Stella Awards were created as a response (by editor Aviva Tuffield and author Sophie Cunnigham).

The latest Miles Franklin award short list contains three females and no males. No pejorative “fest” term has described it as yet. And nor should it, unless one believes that there has been some intrinsic bias in the selection process. Is a result of either zero or five males from five (nominally a 1 in32 shot) evidence of gender bias?

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Concoction, inaccuracy and irrelevance

March 17th, 2009 Chris Lloyd Posted in Probability, Public Interest 6 Comments »

We all know the adage that 57% of statistics quoted in an argument are made up and the other 44% are inaccurate. The third member of the holy trinity of misuse of statistics to my mind is irrelevance. And wrong headed conditioning is usually the best way to end up with an irrelevant statistic. To whit

Domestic violence is the single most likely cause of preventable death for women under the age of 45”.

This week in class, I had an interesting discussion about this statement, which I have heard or seen many times. A few years ago it appeared on billboards around Melbourne and it was in a Green Left Weekly editorial last December.

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ESP and Skepticism

July 29th, 2008 Chris Lloyd Posted in Bayesian, Foundations, Probability 11 Comments »

I often use ESP experiments as a vehicle to introduce hypothesis testing to students. One of the learning outcomes is that statisticians are actually easier to convince of claims of paranormal ability than lay persons . On the basis of a binomial calculation, I tell them that

I would be convinced of ESP if they could correctly select which of 5 cards I am holding more than 75 times out of 250 trials. You get 50 by guessing, and another 25 convinces the hard hearted statistician that you can bend spoons.

But I am actually telling them fibs. I would not be convinced. Does this make me irrational?

more–>I recall many years ago hearing of the so-called Soal experiment, which was in a published in book by Soul and Bateman (1954. Modern Experiments in TelepathyLondon: Faber & Faber) demonstrating telepathy. My first reaction to such claims is always rather skeptical.

In fact, the very evidence that ESP researchers use to convince us of ESP can, under mild assumptions, leads us more strongly to the conclusion that ESP is not true. This is beautifully explained in a 2003 book Probability Theory: The Logic of Science by Edwin T. Jaynes, G. Larry Bretthorst.

The experiment in question involved a woman getting 9410 trails out of 37100 correct where the chance of success under random guessing is p=0.2. This is about 26 standard deviations above the null mean and the binomial P-value is of the order of  1 in 10 to the power 139. But I for one am not convinced that she has ESP.

Frequentist hypothesis testing allows of two hypotheses only. If we are limited to two then, in this case, the relevant ones seem to be HG:she is guessing and p=0.2, or HE: she is not guessing and p>0.2. The null (HG) is overwhelmingly rejected in favour of the conclusion (HE) that she has ESP.

Bayesians can also fall for the trap of considering two hypotheses, in which case they end up in the same place as frequentists. Let LE and LG be the likelihood of the data under the null and alternative. Then the posterior probability she has ESP

eq1

Assuming that our prior probability of ESP, πE , is small, and that the likelihood of the date under the hypothesis of ESP, LE, is not small, the posterior probability of ESP is close to 1 whenever the ratio

r=LG /LE<< πE.

So it merely requires a sufficiently unlikely experimental result to overcome our prior bias against ESP and we will be convinced. So with only two hypotheses, the Bayesians are no better off than the frequentists. If my prior probability of ESP is zero then I will never be convinced. This is still formally rational but resolving to ignore any evidence of whatever strength is not rational in the informal sense.

So am I irrational to not be convinced by the Soal experiment? Do I have a prior probability of zero for ESP? I am here to tell you that I do not.

There are more than two hypotheses in play. Because there are other possible ways the data could have been generated besides a binomial experiment with p=0.2 or p>0.2. There are all sorts of possible deceptions that may have been perpetrated by the woman or by the experimenter. Let us call these various deception mechanisms that generate the data hypotheses and label them HD1,…,H Dk and assign these priors π1,…, πk that are not all extremely small. The posterior of ESP is now 

eq2

where Li is the likelihood of the data under deception hypothesis i. The various deception hypotheses make the observed data reasonably likely, just as the ESP hypothesis does. If we assume that these likelihoods are similar in magnitude to the likelihood LE then the posterior becomes

eq3

where, as before, r=LG/LE measures evidence against guessing compared to ESP. The only terms in this expression that are not likely to be tiny are πG and the πi. So the conclusion is that the posterior of ESP will be close to zero rather than close to 1. Moreover, as the ratio r=LG/LE which we think of as embodying the evidence, becomes smaller, the posterior just reverts to the priors with the guessing hypothesis eliminated. So if we start off thinking that deception is more likely than ESP then we can never be convinced of ESP.

This leads to the conclusion that an honest person may tell the truth about an extremely pertinent experimental results, and not be believed, and those who disbelieve are not being irrational. This statement makes no assumptions about who is actually correct.

The obvious, in fact only, way for ESP researchers to avoid this effect is to force us to reduce our priors on the deception hypotheses. How they could do that, I have no idea.

 

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The Full Monty (Hall)

May 1st, 2008 Chris Lloyd Posted in Cognition, Health Science, Probability, Research/Theory 8 Comments »

The Monty Hall problem is a terrific little example of how intuition, especially the intuition of those trained in statistics, can go horribly wrong. It appears from recent calculations of the economist, M. Keith Chen that some of the most famous experiments in psychology may have fallen victim to the same logical fallacy.

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False Convictions

April 15th, 2008 Chris Lloyd Posted in Probability, Public Interest No Comments »

How often are innocent people convicted? Our justice system, and that of the US, is supposed to be weighted heavily against the possibility of type 1 errors. A recent article in the NY Times highlights some faulty reasoning by well-known conservative Antonin Scalia, who says we call all sleep peacefully knowing the rate of false conviction is only 0.00027.

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A long way to go II

December 13th, 2007 Chris Lloyd Posted in Probability, Public Interest 1 Comment »

This will be my last post for 2007. Have a great break folks. The interview with John Croucher below is from the ABC’s law report last month. If you prefer you can download the audio version Here (13Mb).

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A Right Royal Statistical Fallacy*

November 28th, 2007 Chris Lloyd Posted in Fun Stuff, Politics, Probability, Public Interest No Comments »

The inquest into Princess Diana’s death is a great wastes of public money. but in addition to fuel for the tabloids, it has supplied me with a nice little example of wrong statistical reasoning. The culprit is (of course!) an expert witness but not apparently an expert in thinking about uncertainty. The expert in question is a Dr. John Searle, a traffic engineer hired by the Ritz hotel which is owned by Dodi al Fayed’s father. In other words he has really been hired to support al Fayed’s conspiracy theory that Diana was murdered which requires him first to discredit the theory that Diana’s driver being pissed was the cause of the tragedy.

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An enigma in evaluating DNA Evidence

March 16th, 2007 Chris Lloyd Posted in Probability, Public Interest, Science 3 Comments »

DNA evidence of a match between a suspect and a sample at the crime scene are often presented as evidence in court. Roughly speaking, if there is a probability “p” of someone matching the crime scene sample by luck, then the LR contribution from the DNA evidence turns out to be 1/p. So you can think of “p” as the significance of the match. In many cases, the match may be have been obtained by checking through a database of known criminals. The question obviously arises: If the match comes from trawling a large database, doesn’t this affect the weight of evidence to be assigned? As statisticians we are familiar with the dangers of trawling the data. Shouldn’t the jury at least know that the match was obtained this way?

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