Difference Between Parametric and Non-Parametric Tests

In the field of research, different tools are used that allow the analysis of the information to be obtained. According to the object or subject under study, different data treatment options are presented, which are given considering the particular characteristics of what is intended to be studied. It is there where statistics offers a wide range of strategies that facilitate the handling of information to the researcher, who must select the most appropriate to the needs of the study. Considering the parameters of the data, we have two options: parametric and non-parametric tests.

What are Parametric Tests?

Parametric tests are one of the statistical tools in which the data of a continuous quantitative variable is analyzed, which must be represented by a natural or decimal number, which allows its manipulation with mathematical formulas, managing to explain its behavior; so it is used for the study of some aspects of the population.

For the application of parametric tests, it is necessary to have a fairly large sample, since this guarantees greater accuracy in the calculations; Likewise, the selection of the sample must be done randomly through a normal or Gaussian probability distribution.

There are a number of aspects that are essential to consider before applying a parametric test, and for it to have the validity and reliability that a study with scientific rigor requires, the sample must have the following characteristics:

Normality: the values it presents are located mostly around a central point, or the value considered normal, which indicates a regular behavior of the variable.
Homogeneity: the characteristics of the sample must be similar.
Errors: these being independent, that is why we work with random samples.

What are Non-Parametric Tests?

On the other hand, nonparametric tests refer to the statistical analysis of a sample that does not meet the standard parameters of regularity and are resistant to transformations. In general, this type of test is used when you want to test a hypothesis, but there is the possibility of modifying it according to the results of the analysis of the variables.

For the application of non-parametric tests, a small sample can be used and it is not required that the values under study be around a central point, but the behavior of the data is inferred without knowing the mean, however there must be some equality between the samples with respect to their shape and dispersion.

Studies that use nonparametric tests can handle quantitative variables but can also be applied to qualitative data, which have ordinal or nominal variables. For this reason, non-parametric tests are usually the most used by the branches of social sciences that deal with psychology and human behavior.