Why use a non parametric test?

When to use it

Non parametric tests are used when your data isn't normal. Therefore the key is to figure out if you have normally distributed data. For example, you could look at the distribution of your data. If your data is approximately normal, then you can use parametric statistical tests.


Regarding this, why are non parametric tests less powerful?

Nonparametric tests are less powerful because they use less information in their calculation. For example, a parametric correlation uses information about the mean and deviation from the mean while a nonparametric correlation will use only the ordinal position of pairs of scores.

Also Know, should I use parametric or nonparametric test? If the mean more accurately represents the center of the distribution of your data, and your sample size is large enough, use a parametric test. If the median more accurately represents the center of the distribution of your data, use a nonparametric test even if you have a large sample size.

Also question is, what are the advantages and disadvantages of non parametric test?

That's another advantage of non-parametric tests. The main disadvantages are 1) Lack of statistical power if the assumptions of a roughly equivalent parametric test are valid, 2) Unfamiliarity and 3) Computing time (many non-parametric methods are computer intensive).

What is non parametric analysis?

Nonparametric statistics refer to a statistical method in which the data is not required to fit a normal distribution. Nonparametric statistics uses data that is often ordinal, meaning it does not rely on numbers, but rather on a ranking or order of sorts.

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