Originally Posted by Ubercroz
Over a large enough sample the two should converge
yeah think this is probably what duggs is saying regarding the central limit theorem aspect as well in regards to the distribution normalizing where it will fall on a bell curve around the 80%.
Again, I had to google and look up the central limit theorem.....this is fun i quite enjoy learning especially when its interesting, and probably not really contributing here either but its got some mice trying to spin the wheel in my brain
In probability theory, the central limit theorem (CLT) states that, given certain conditions, the arithmetic mean of a sufficiently large number of iterates of independent random variables, each with a well-defined expected value and well-defined variance, will be approximately normally distributed. That is, suppose that a sample is obtained containing a large number of observations, each observation being randomly generated in a way that does not depend on the values of the other observations, and that the arithmetic average of the observed values is computed. If this procedure is performed many times, the central limit theorem says that the computed values of the average will be distributed according to the normal distribution (commonly known as a "bell curve").