Inliers is the term used for the observations most likely to be subject to error in situations where the dichotomy is developed by making a ‘cut’ on an ordered scale, and where the errors of classification can be expected to happen with greatest frequency in the neighbourhood of cut. Assume, for instance, that the individuals are classified say on a hundred point scale which indicates measure of illness. A cutting point is chosen on the scale to dichotomize the individuals into well and ill categories. Errors of classification are surely to occur in the neighbourhood of the cutting point.