ABSTRACT
Generally speaking, statistical methodology is split into two broad classes: parametric and nonparametric. Parametric methods assume that forms of distributions of random variables are known and only a fixed number of parameters of these distributions must be estimated. In many applications of parametric methods, even if the exact form of the distribution is not known, observations come from a sufficiently large sample, thus the use of the normal approximation is validated by the Central Limit Theorem. In the situations when the sample size is relatively small and no conjecture may be made regarding the functional form of the underlying distribution, nonparametric methods are used as an alternative.
