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392 points seanhunter | 1 comments | | HN request time: 0.209s | source
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japoco ◴[] No.42184331[source]
This is probably just because the coins aren’t actually fair. If the coin is slightly biased towards heads, the first throw is more likely to heads, and so are all subsequent throws. Same for tails.
replies(2): >>42184385 #>>42184417 #
onion2k ◴[] No.42184417[source]
That's the opposite of what the paper says. If the coin was biased you'd expect it to land on heads more often regardless of what side it starts on. The coins land on the side they start on more often.
replies(1): >>42184484 #
japoco ◴[] No.42184484[source]
No, first of all due to imperfections in the manufacture of real coins, there are actually no fair coins. Also the bias in the probability affects the first throw as well as all the rest. If your dataset is composed of first throws/rest of the throws, you’re going to see they are correlated.
replies(2): >>42184948 #>>42185389 #
1. sigbottle ◴[] No.42184948[source]
I think you're missing the fact that you don't have to chain coin flips literally right after another.

As the other commenter said, in between coin flips, use a highly secure PRNG to orient the coin randomly. This would correct for your bias (if true).