Quote:
Originally Posted by Sirken
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btw, your poker analogy is good, but doesnt apply because you are removing cards from the deck, hence every round your odds of drawing to that flush change for better or worse depending on the previous card or cards put out (depending which game ur playing).
a closer analogy would be blackjack, but with a brand new deck for each hand.
each pop (be it ph or the rare mob) is a completely separate encounter, and what pops has absolutely 0% to do with what popped before that.
not trying to be a dick, just explaining [You must be logged in to view images. Log in or Register.] |
Just a side note on the Gambler's Fallacy.
Gambler's Fallacy is only the wrong assumption that any
individual random event is influenced by the previous random events.
However, it is true that over long periods of time and large values of
n the binomial distribution will approach the probability of the possible outcomes. This is in fact how probabilities are deduced.
It is correct to say that it is increasingly improbable for the set of
n observations to deviate significantly from the distribution as
n increases.
So, if there is a probability of 5% (p=0.05) of an event producing a 1, and all other times (p=0.95) will produce a 0, then at n=1,000,000 you will have a distribution that is very close to the probability.
In other words, if you don't have very close to 50,000 1s, something is seriously broken.
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Binomial Distribution
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Law of Large Numbers
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Gambler's Fallacy