Re:Why We Need a Statistical Revolution
by rcthompson on Sunday May 17, 2015 @06:42PM
(#9562020)
The assumption is reasonable for a lot of cases because of the central limit theorem. In particular, it applies if the variable of interest is the sum of a large number of independent random variables. For example, the number of heads in N coin flips is the sum of the number of heads in each of N independent experiments each consisting of a single flip.
Of course, there are also a lot of important cases where that N isn't quite large enough to justify the normal distribution, or where the quantity of interest is not a sum of a large number of small effects, so the CLT doesn't apply at all.
For example if you work with latencies then the normal distribution is a terrible thing to use. The closer to zero you get the more skewed it has to be, simply because everything that can influence the result will mostly change it in one direction.
So in that particular case an Erlang distribution might be more applicable. And even that often falls short if you have periodic things (background tasks) occasionally messing with your latency, creating a multi-modal distribution.