Also, thanks for the reference to Brown’s paper “Inference Causation”.

I may be three years out of date, but here’s my comment on Brown’s paper. His primary argument against Bayes Theorem is that one must carry the influence of a subjectively chosen prior through to the final posterior probability estimates is questionable, to say the least. The whole process can be conducted without ever selecting prior distributions. To do this, work instead with the likelihood ratio: Probability(guilty)/Probability(innocent), this ratio will either approach 1, indicating guilt, or 0, indicating innocence.

I don’t know about law, but I do know about stats.

I don’t know anything about the gender ratio of recent law graduates, nor do I know how many recent grads you’d be likely to see. So, I’ll make up some numbers.

Case 1. Suppose there are 100 recent grads that you might see, and that 50 are male and 50 are female. The probability you see two males is the same as the probability you see to females: (50×49)/(100×99) = 0.247474. The probability you see a male-female, (or female-male pair) is: (50x50x2)/(100×99) = 0.5050505. Pretty close to 1/4, 1/2, 1/4.

Case 2. Suppose there are 100 recent grads that you might see, and that 25 are male and 75 are female. The probability you see two males is: (25×24)/(100×99) = 0.060606. The probability you see two females is (75×74)/(100×99) = 0.56060606. The probability you see a male-female, (or female-male pair) is (25x75x2)/(100×99) = 0.3787878. Nowhere near 1/4, 1/2, 1/4.

nice blog,

alison