# What is "normality"?

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 Revision as of 03:31, 17 February 2008 (view source)Doug (Talk | contribs)← Older edit Latest revision as of 20:54, 7 September 2009 (view source)Doug (Talk | contribs) (2 intermediate revisions not shown) Line 1: Line 1: *'''What is "normality"?''' *'''What is "normality"?''' *#A normal distribution is a symmetric bell-shaped curve defined by two things: the mean (average) and variance (variability). There are an infinite number of normal distributions because there are an infinite number of permutations of the mean and variance. *#A normal distribution is a symmetric bell-shaped curve defined by two things: the mean (average) and variance (variability). There are an infinite number of normal distributions because there are an infinite number of permutations of the mean and variance. - *#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. - *#To provide a rough example of normality and non-normality, see the following histograms. The black line superimposed on the histograms represents the bell-shaped "normal" curve. Notice how the data for variable1 are  normal, and the data for variable2 are non-normal. In this case, the non-normality is driven by the presence of an outlier. For more information about outliers, see [[What are outliers?]], [[Detecting Outliers - Univariate | How do I detect outliers?]], and [[Dealing with Outliers | How do I deal with outliers?]]. + *#[[Image:Fe40.png]] - To provide a rough example of normality and non-normality, see the following histograms. The black line superimposed on the histograms represents the bell-shaped "normal" curve. Notice how the data for variable1 are  normal, and the data for variable2 are non-normal. In this case, the non-normality is driven by the presence of an outlier. For more information about outliers, see [[What are outliers?]], [[Detecting Outliers - Univariate | How do I detect outliers?]], and [[Dealing with Outliers | How do I deal with outliers?]].
[[Image:V1hn0.png|350px]][[Image:V2hnn0.png|350px]]
[[Image:V1hn0.png|350px]][[Image:V2hnn0.png|350px]]
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## Latest revision as of 20:54, 7 September 2009

• What is "normality"?
1. A normal distribution is a symmetric bell-shaped curve defined by two things: the mean (average) and variance (variability). There are an infinite number of normal distributions because there are an infinite number of permutations of the mean and variance.
2. 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.
3. - To provide a rough example of normality and non-normality, see the following histograms. The black line superimposed on the histograms represents the bell-shaped "normal" curve. Notice how the data for variable1 are normal, and the data for variable2 are non-normal. In this case, the non-normality is driven by the presence of an outlier. For more information about outliers, see What are outliers?, How do I detect outliers?, and How do I deal with outliers?.  ◄ Back to Analyzing Data page