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# Difference between parametric and non parametric tests

The nonparametric tests are those that are responsible for analyzing data that do not have a particular distribution and assumptions are based, but the data are not organized normally. Although they have some limitations, they have ordered statistical results that facilitate their understanding. Difference between parametric and non parametric tests

The nonparametric tests , however, are based on the laws of normal distribution to analyze the elements of a sample. Generally, they only apply to numerical variables and for your analysis you should keep a large population, since it allows the calculation to be more exact.

## Differences between nonparametric tests and parametric tests

Nonparametric tests Parametric tests
Greater statistical power. Lower statistical power.
They are applied in categorical variables. They are applied to normal or interval variables.
They are used for small samples. They are used for large samples.
The form of data distribution is not known. Its data distribution is normal.
They don’t make a lot of assumptions. They make a lot of assumptions.
They require a lower condition of validity. They require a higher condition of validity.
Higher probability of errors. Lower probability of errors.
The calculation is less complicated to do. The calculation is complicated to do.
The hypotheses are based on ranges, median and frequency of data. The hypotheses are based on numerical data.
The calculations are not exact. The calculations are too exact.
Consider missing values ​​for information. It does not take missing values ​​into account to obtain information.

Before applying non-parametric tests or parametric tests, it is important to know aspects such as the objective of the investigation, the size of the population and the scale that will be used to measure the data. Difference between parametric and non parametric tests

It is likely that the data does not meet the requirements required by a parametric test and a non-parametric test will have to be chosen, that is, the sample size is small or the distribution is not normal.

Another factor to consider is that parametric tests can use an abnormal distribution, but a nonparametric one has extremely strict assumptions that cannot be ignored.

Finally, if the sample size is small, you will most likely not get results if you use a nonparametric test. When the population is not really large, the chances of identifying a significant effect are lower.

Difference between parametric and non parametric tests

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