# Why is normality important?

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*#Most statistical tests rest upon the assumption of normality. Deviations from normality, called non-normality, render those statistical tests inaccurate, so it is important to know if your data are normal or non-normal. | *#Most statistical tests rest upon the assumption of normality. Deviations from normality, called non-normality, render those statistical tests inaccurate, so it is important to know if your data are normal or non-normal. | ||

*#Tests that rely upon the assumption or normality are called parametric tests. If your data is not normal, then you would use statistical tests that do not rely upon the assumption of normality, call non-parametric tests. Non-parametric tests are less powerful than parametric tests, which means the non-parametric tests have less ability to detect real differences or variability in your data. In other words, you want to conduct parametric tests because you want to increase your chances of finding significant results. | *#Tests that rely upon the assumption or normality are called parametric tests. If your data is not normal, then you would use statistical tests that do not rely upon the assumption of normality, call non-parametric tests. Non-parametric tests are less powerful than parametric tests, which means the non-parametric tests have less ability to detect real differences or variability in your data. In other words, you want to conduct parametric tests because you want to increase your chances of finding significant results. | ||

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## Latest revision as of 20:54, 7 September 2009

**Why is normality important?**- Most statistical tests rest upon the assumption of normality. Deviations from normality, called non-normality, render those statistical tests inaccurate, so it is important to know if your data are normal or non-normal.
- Tests that rely upon the assumption or normality are called parametric tests. If your data is not normal, then you would use statistical tests that do not rely upon the assumption of normality, call non-parametric tests. Non-parametric tests are less powerful than parametric tests, which means the non-parametric tests have less ability to detect real differences or variability in your data. In other words, you want to conduct parametric tests because you want to increase your chances of finding significant results.

◄ Back to Analyzing Data page