  The brilliant method due to Good and Turing allows for estimating objects not
occurring in a sample. The problem, known under names "sample coverage" or
"missing mass" goes back to their cryptographic work during WWII, but over
years has found has many applications, including language modeling, inference
in ecology and estimation of distribution properties. This work characterizes
the maximal mean-squared error of the Good-Turing estimator, for any sample
\emph{and} alphabet size.
