This article is from the Puzzles FAQ, by Chris Cole chris@questrel.questrel.com and Matthew Daly mwdaly@pobox.com with numerous contributions by others.

A cab was involved in a hit and run accident at night. Two cab companies,

the Green and the Blue, operate in the city. Here is some data:

a) Although the two companies are equal in size, 85% of cab

accidents in the city involve Green cabs and 15% involve Blue cabs.

b) A witness identified the cab in this particular accident as Blue.

The court tested the reliability of the witness under the same circumstances

that existed on the night of the accident and concluded that the witness

correctly identified each one of the two colors 80% of the time and failed

20% of the time.

What is the probability that the cab involved in the accident was

Blue rather than Green?

If it looks like an obvious problem in statistics, then consider the

following argument:

The probability that the color of the cab was Blue is 80%! After all,

the witness is correct 80% of the time, and this time he said it was Blue!

What else need be considered? Nothing, right?

If we look at Bayes theorem (pretty basic statistical theorem) we

should get a much lower probability. But why should we consider statistical

theorems when the problem appears so clear cut? Should we just accept the

80% figure as correct?

probability/cab.s

The police tests don't apply directly, because according to the

wording, the witness, given any mix of cabs, would get the right

answer 80% of the time. Thus given a mix of 85% green and 15% blue

cabs, he will say 20% of the green cabs and 80% of the blue cabs are

blue. That's 20% of 85% plus 80% of 15%, or 17%+12% = 29% of all the

cabs that the witness will say are blue. Of those, only 12/29 are

actually blue. Thus P(cab is blue | witness claims blue) = 12/29.

That's just a little over 40%.

Think of it this way... suppose you had a robot watching parts on a

conveyor belt to spot defective parts, and suppose the robot made a

correct determination only 50% of the time (I know, you should

probably get rid of the robot...). If one out of a billion parts are

defective, then to a very good approximation you'd expect half your

parts to be rejected by the robot. That's 500 million per billion.

But you wouldn't expect more than one of those to be genuinely

defective. So given the mix of parts, a lot more than 50% of the

REJECTED parts will be rejected by mistake (even though 50% of ALL the

parts are correctly identified, and in particular, 50% of the

defective parts are rejected).

When the biases get so enormous, things starts getting quite a bit

more in line with intuition.

For a related real-life example of probability in the courtroom see

People v. Collins, 68 Cal 2d319 (1968).

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