In all earlier chapters we dealt with parametric models, and described inferential methods for testing and estimating model parameters, prediction intervals and tolerance intervals. In this chapter, we shall see some basic inferential methods for continuous distributions without assuming any parametric models. In other words, the results in the following sections are applicable to any continuous population. Furthermore, if an inferential method is not available for a parametric continuous distribution, then one could safely use an appropriate nonparametric method. However, it should be noted that the nonparametric methods are less efficient than their parametric counterparts. If a sample data from a population fits a parametric model, such as a normal distribution, it is better to use the inferential methods applicable to that model.