The amount of time it takes to solve a block is bounded by a normal distribution.
Well... not exactly.
A duration time T can *never* be normally distributed, since T can't be zero nor less than zero.
We can better say that the number of trials it takes to solve a block is
geometrically distributed:
http://en.wikipedia.org/wiki/Geometric_distribution.
The amount of time required by the solution is the product of the amount of time required by each trial (which in ideal conditions is the same for every given mining device) times the number of trials: so it's also geometrically distributed. We call this geometric distribution "the distribution of the population".
However we can take a different approach: we will run a mining device and write down how much it will take to solve the first block, than the second block, than the third (always with the same difficulty, of course) and so on N times. At the end we will get a set of N values (N durations), but for now we don't have any values, we have a set of... N random variables with the same geometrical distribution of the population! This set of random variables is called "a sample".
Let's sum these random variables, and then divide the sum by their number, N. We get another random variable: the "sample mean".
We don't have any number yet, just variables.
Being the "sample mean" a sum of random variables (identically distributed), we can apply to it the central limit theorem:
http://en.wikipedia.org/wiki/Central_limit_theorem.
So a "sample mean" (regardless of its population) is always
asymptotically bounded by a normal distribution (in practice, and in the worst case, 50 observations are more than enough).
The "sample mean" expected value is the same of the population, and its variance is the population variance divided by N.
I'm sure you know it all, but maybe someone was confused.
HTH.