Add up enough independent random things and the total tends towards a bell curve, regardless of what the individual things looked like. It is why averages are well behaved even when individual measurements are not, and it is the licence behind a great deal of routine statistics.
Technical view
The normalised sum of independent, identically distributed variables with finite variance converges to a normal distribution. The conditions matter: it fails for heavy-tailed distributions with infinite variance, and convergence is slow for skewed data, so small-sample normal approximations can be poor. It justifies confidence intervals for means and underlies the practice of averaging across evaluation runs.
Related
Part of AI Terminology Index, section: Maths and Statistics.