We give a new characterization of inverse Gaussian distributions using the regression of a suitable statistic based on a given random sample. A corollary of this result is a characterization of inverse Gaussian distribution based on a conditional joint density function of the sample. Application of this corollary as a transformation in the procedure to construct EDF (empirical distribution function) goodness-of-fit tests for inverse Gaussian distributions is also studied.