Wednesday, 29 January 2014

Lecture 6

Here is some interesting reading for you about the law courts and conditional probability.
  • A formula for justice
    Bayes' theorem is a mathematical equation used in court cases to analyse statistical evidence. But a judge has ruled it can no longer be used. Will it result in more miscarriages of justice?
  • Court of Appeal bans Bayesian probability (and Sherlock Holmes)
    In a recent judgement the English Court of Appeal has not only rejected the Sherlock Holmes doctrine shown above, but also denied that probability can be used as an expression of uncertainty for events that have either happened or not.
  • Prosecutor's fallacy
    The prosecutor's fallacy is a fallacy of statistical reasoning, typically used by the prosecution to argue for the guilt of a defendant during a criminal trial. 
I mentioned David Aldous's blog post Presenting probability via math puzzles is harmful.
Do you think it has harmed you to see the problem "I have two children one of whom is a boy (born on a Thursday)"?

If you read the Wikipedia article on Simpson's paradox you will find that an alternative name is the Yule-Simpson effect. Udny Yule (a Fellow of St John's College, and a lecturer in statistics at Cambridge) appears to be the first one to have commented on this phenomenon (in 1903).

Our Example 6.7 was artificial, so it would be easy to see what was happening (women predominated amongst independent school applicants and women were more likely to gain admission.) In the Wikipedia article you can read about more practical examples. One of the best-known real-life examples of Simpson's paradox occurred when the University of California, Berkeley was sued for bias against women who had applied for admission to graduate schools there.

I remember once extracting data from Cambridge University Tripos results to show that in each of certain set of carefully selected subjects (including Engineering, English, ...) women were more likely to obtain Firsts than men, but that men were more likely to obtain Firsts when results over all subjects were combined. This sounds paradoxical. Can you figure out how it can happen? Hint: some subjects award greater percentages of Firsts than do others. Some subjects are relatively more popular with women. 

Comments (5)

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I think one point which make information like "I have at least one son born on a Thursday" confusing is not being given context for the information. For example say I have a computer game with 100 levels and someone playing is equally likely to die on each level. If my friend tells me "I got passed level 40 today" should we really interpret this as meaning that we should now take the distribution of the level he lost on to be uniform on [41,100]?
1 reply · active 580 weeks ago
I think it matters how you believe he chooses to say 40. He might have decided that he would say 40 provided he lost on some level 41 or higher. Or he might have decided that upon reaching level x he is going to randomly choose some level 1 to x and then tell you "I got past level x". In the same manner, we could get different answers after hearing "I have a son born on a Thursday", depending on how we think he chooses to tell us this information. But we can avoid this problem, by imagining that we are one asking him questions. We know he has two children. In one experiment we ask "Do you have a son?". In another experiment we ask "Do you have a son born on a Thursday?" Given the answer yes, we will place different probabilities on his having two sons.
Hi, I'm just a bit confused about your example for 6.3 in the notes." Let px be the probability he goes broke before reaching a" -- is Px the probability he goes broke at the xth toss?
2 replies · active 580 weeks ago
No. p_x is the probability that he goes broke at some time before reaching a. That is, he hits 0 before hitting a. This is the same thing as what we called q_z in Section 15.2 (page 59). We found q_z=(a-z)/a, which is identical to the result in 6.3.
Oh ok, thank you

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