# Difference Between 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.

## Difference Between Parametric and Non-Parametric Tests

 parametric tests Nonparametric tests Applies to quantitative variables (interval and ratio) Applies to qualitative variables (nominal and ordinal) The sample must be large The sample to be used can be small The data is distributed around a central point. The distribution of the data is unknown. Requires greater control of the sample for its validity Has few requirements to validate the sample Uses complex methods to achieve data analysis It uses data packages that show the behavior of the data, facilitating its analysis Bet on the accuracy of the results, with a very low margin of error Does not require accuracy in the results, so it is more prone to errors