# How do I deal with non-normality?

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*#Leave your data non-normal, and conduct the '''non-parametric tests''' designed for non-normal data. Non-parametric tests are specifically designed to be accurate in the presence of non-normal data, but the disadvantage is non-parametric tests typically have lower power than parametric tests. Power is the ability to detect real differences or variability in your data, so researchers typically want to conduct parametric tests because you want to increase your chances of finding significant results. | *#Leave your data non-normal, and conduct the '''non-parametric tests''' designed for non-normal data. Non-parametric tests are specifically designed to be accurate in the presence of non-normal data, but the disadvantage is non-parametric tests typically have lower power than parametric tests. Power is the ability to detect real differences or variability in your data, so researchers typically want to conduct parametric tests because you want to increase your chances of finding significant results. | ||

*#Conduct '''“robust” tests'''. There is a growing branch of statistics called “robust” tests that are just as powerful as parametric tests but account for non-normality of the data. | *#Conduct '''“robust” tests'''. There is a growing branch of statistics called “robust” tests that are just as powerful as parametric tests but account for non-normality of the data. | ||

- | *#'''Transform the data'''. Transforming your data involving using mathematical formulas to modify the data into normality. | + | *#'''Transform the data'''. Transforming your data involving using mathematical formulas to modify the data into normality. See [[How do I transform variables?]] for more information. |

- | + | ||

+ | FYI - If your data are non-normal due to outliers, see [[Dealing with Outliers]] because you have other options (than the four options above) if your non-normality is due exclusively to outliers. | ||

## Revision as of 04:38, 17 February 2008

**How do I deal with non-normality?**- If your data are non-normal, you have four basic options to deal with non-normality:

- Leave your data non-normal, and conduct the
**parametric tests**that rely upon the assumptions of normality. Just because your data are non-normal, does not instantly invalidate the parametric tests. Normality (versus non-normality) is a matter of degrees, not a strict cut-off point. Slight deviations from normality may render the parametric tests only slightly inaccurate. The issue is the degree to which the data are non-normal. - Leave your data non-normal, and conduct the
**non-parametric tests**designed for non-normal data. Non-parametric tests are specifically designed to be accurate in the presence of non-normal data, but the disadvantage is non-parametric tests typically have lower power than parametric tests. Power is the ability to detect real differences or variability in your data, so researchers typically want to conduct parametric tests because you want to increase your chances of finding significant results. - Conduct
**“robust” tests**. There is a growing branch of statistics called “robust” tests that are just as powerful as parametric tests but account for non-normality of the data. **Transform the data**. Transforming your data involving using mathematical formulas to modify the data into normality. See How do I transform variables? for more information.

FYI - If your data are non-normal due to outliers, see Dealing with Outliers because you have other options (than the four options above) if your non-normality is due exclusively to outliers.

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