  Given n observations, we study the consistency of a batch of k new
observations, in terms of their distribution function. We propose a
non-parametric, non-likelihood test based on Edgeworth expansion of the
distribution function. The keypoint is to approximate the distribution of the
n+k observations by the distribution of n-k among the n observations. Edgeworth
expansion gives the correcting term and the rate of convergence. We also study
the discrete distribution case, for which Cram\`er's condition of smoothness is
not satisfied. The rate of convergence for the various cases are compared.
