  Quantitative properties of stochastic systems are usually specified in logics
that allow one to compare the measure of executions satisfying certain temporal
properties with thresholds. The model checking problem for stochastic systems
with respect to such logics is typically solved by a numerical approach that
iteratively computes (or approximates) the exact measure of paths satisfying
relevant subformulas; the algorithms themselves depend on the class of systems
being analyzed as well as the logic used for specifying the properties. Another
approach to solve the model checking problem is to \emph{simulate} the system
for finitely many runs, and use \emph{hypothesis testing} to infer whether the
samples provide a \emph{statistical} evidence for the satisfaction or violation
of the specification. In this short paper, we survey the statistical approach,
and outline its main advantages in terms of efficiency, uniformity, and
simplicity.
