Parametric and nonparametric statistics

Abstract

    It is known that the parameter statistics are more used and accurate than non-parametric, and despite this there is a lot of controversy about the use of either of these two types in multiple statistical treatments, and therefore the topic was discussed from several directions.

The parameter means an attribute or characteristic of a given population against an estimate, which is a characteristic of a sample, and the most important feature of statistics with parameterized from non-parameters is the arithmetic mean and standard deviation. From here, parameter statistics can be understood as a set of methods that require the fulfillment of specific assumptions about the population from which the sample is withdrawn. Therefore, nonparametric statistics are a set of alternative methods that are used in cases where the assumptions are about the community from which the sample was not drawn or in the case of nominal data. And ordinal, and both parametric and non-parametric statistics, each of them enjoy a certain level of confidence that is determined in light of the available data, as well as the conditions that fulfill the assumptions and therefore are inferential statistical methods whose results can be generalized to society.