One of the disadvantages of DEA is that statistical inference is difficult to be applied due to the implied assumption of the method that the whole distance of a DMU from the efficient frontier reflects solely its inefficiency. In reality, this distance reflects inefficiency as well as sampling variability and noise because input and output data are normally subject to errors. Furthermore, given the assumption that noise does not exist, the estimated empirical technology can only be a subset of the true but unknown technology, and therefore DEA scores will be upwards biased . In order to overcome this shortcoming of biased DEA scores due to sampling variability Simar and Wilson  proposed a methodology, which is based on bootstrap techniques  and allows determining the statistical properties of DEA estimators.