ABSTRACT
An important problem in statistics relates to obtaining information about the form of the population from which a sample is drawn. The shape of this distribution or some inference concerning a particular aspect of the population may be of primary interest. In this latter case, in classical statistics, information about the form generally must be assumed or incorporated in the null hypothesis to perform an exact parametric type of inference. The compatibility of a set of observed sample values with a normal or any other distribution can be checked by a goodness-of-fit test. These tests are designed for a null hypothesis, which is a statement about the form of the cumulative distribution or probability function of the parent population from which the sample is drawn. When the sample observations are quantitative, the categories would be numerical classes chosen by the experimenter.
