  Monte Carlo approximations for random linear elliptic PDE constrained
optimization problems are studied. We use empirical process theory to obtain
best possible mean convergence rates $O(n^{-\frac{1}{2}})$ for optimal values
and solutions, and a central limit theorem for optimal values. The latter
allows to determine asymptotically consistent confidence intervals by using
resampling techniques.
