Talk:Independent and identically-distributed random variables

From a practical point of view, an important implication of this is that if the roulette ball lands on 'red', for example, 20 times in a row, the next spin is no more or less likely to be 'black' than on any other spin.

I disagree with the above statement. The sequence is IID by definition, so it does not matter whether or not the previous 20 rolls resulted in red. — Preceding unsigned comment added by 199.43.48.131 (talk)

So what do you disagree with? You say it doesn't matter; the statement you say you disagree with also says it doesn't matter. Michael Hardy 22:50, 29 October 2007 (UTC)[reply]

The uninlogged IP 199.43.48.131 has been blocked for vandalism a number of times, and has not been able to explain their criticism to this page in a sensible manner. I therefore remove the {{unreferenced}} tag put on the page by said IP.-JoergenB (talk) 14:03, 19 January 2008 (UTC)[reply]

<< central limit theorem, which states that the probability distribution of the sum (or average) of i.i.d. variables with finite variance approaches a normal distribution, becoming acceptably close when sample size n \geq 30. >>

What is n? If it's the number of variables added, this statement is obviously false in general: just take a very heavy-tailed distribution. It might be true if you restrict it to uniform distributions, though. Guslacerda (talk) 05:35, 18 February 2008 (UTC)[reply]

Better?-JoergenB (talk) 02:43, 19 February 2008 (UTC)[reply]