Coin flip probabilities and relevance

[TechnocratX]

I know this thread is over 8 years old, but this reply is for the benefit of someone like me who stumbles across it. Plus I think I can explain it in a more simpler manner, especially for those with basic stats knowledge.

Concerning coin flip probabilities...

For example, if out of 10,000 coin flips, I get 9000 heads, then for the next 10,000 flips, the distribution of heads vs. tails would not be 50/50, but would be weighed in favor of more tails in order to get back to the 50/50 mean.

I call such a change in normal tendency as "probability pressure" (PP)on the "probability wave" (PW). I realize the term probability wave is already established in reference to light, but it seems to apply here.

Any thoughts, suggestions, comments

Ok, say you did the first 10,000 coin flips, and got 9000 heads. This gives you a 90/10 distribution. Now you're thinking you're at the top of a heads wave, and should expect a tail wave to take you back to a 50/50 distribution.

Then you carry on and do another 1,000,000 coin flips, but this time you get exactly 500,000 heads and 500,000 tails. So no increase in tails from a pressure wave. But, even without the tail pressure wave your graph has now moved to a 50.4/49.6 distribution.

What's happened is that you've simply increased the sample size and that has reduced the effect of the 9000 heads. Hopefully you can now see that the wave patterns tending towards the 50/50 distribution, are caused by the increase in samples and not an increase in heads or tails through a pressure wave.