At any scale, the limited size of a study area introduces a bias in the interpretation of linear features, defined as right-censoring bias. We show the effects of not considering such bias and apply survival analysis techniques to obtain unbiased estimates of multiple parametrical distributions in three censored length datasets. Finally, we propose a novel approach to select the most representative model from a sensible candidate pool using the probability integral transform technique.