By its nature, Data Envelopment Analysis (DEA) leaves no room for uncertainty in data such as measurement errors. To improve this fact, we consider $\alpha$-stochastic efficiency concept, and we relate this problem to the stochastic programming problem. Probability inequalities are employed for introducing new criteria, and two special cases for normal and for general distribution are discussed. The strengths of new criteria
are illustrated with a numerical example.