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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