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Amusing comment, however a fair coin is not double sided by definition. :) In any case, I changed the example from 30 to 10 tosses, so that we avoid "missing t
by acangiano 17y ago
Amusing comment, however a fair coin is not double sided by definition. :)
In any case, I changed the example from 30 to 10 tosses, so that we avoid "missing the forest for the trees".
- nkurz 17y agoBut what does a 'fair coin' have to do with bets in the real world? Assume I'm standing on a street corner, flipping a coin . You observe 30 flips, and note that 30 times in a row it comes up heads. I then turn and offer to bet you 2:1 odds that the next flip will also be heads. Would you really take my bet? No, you would walk away, despite your superior knowledge of probability. But let's say that it was only 10 flips in a row, as in your modified example. Would you stay and bet? If you would, perhaps it is you is is missing the forest. :) More precisely, perhaps the problem is not their knowledge of probability, but the difficulty of modeling the real world with standard probability theory. The Bayesian approach above at least allows for the possibility that the coin is not fair. How does your model allow for this?