  Thanks to the increasing availability in computing power, high-dimensional
engineering problems seem to be at reach. But the curse of dimensionality will
always prevent us to try out extensively all the hypotheses. There is a vast
literature on efficient methods to construct a Design of Experiments (DoE) such
as low discrepancy sequences and optimized designs. Classically, the
performance of these methods is assessed using a discrepancy metric. Having a
fast discrepancy measure is of prime importance if ones want to optimize a
design. This work proposes a new methodology to assess the quality of a random
sampling by using a flavor of Newcomb-Benford's law. The performance of the new
metric is compared to classical discrepancy measures and showed to offer
similar information at a fraction of the computational cost of traditional
discrepancy measures.
