How do I deal with non-normality?
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Revision as of 04:30, 17 February 2008 by Doug
- How do I deal with non-normality?
- If your data are non-normal, you have four basic options to deal with non-normality:
- Option 1 is to 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.
- Option 2 is to leave your data non-normal, and conduct the non-parametric tests designed for non-normal data.
- Option 3 is to 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.
- Option 4 is to transform the data. Transforming your data involving using mathematical formulas to modify the data into normality.
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