What is "normality"?
From PsychWiki - A Collaborative Psychology Wiki
(Difference between revisions)
| Line 3: | Line 3: | ||
*#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?]]. | *#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?]]. | ||
| - | <center><table><td>[[Image:V1hn0.png|350px]]</td><td>[[Image:V2hnn0.png|350px]]</td><table> | + | <center><table><td>[[Image:V1hn0.png|350px]]</td><td>[[Image:V2hnn0.png|350px]]</td><table></center> |
| - | + | ||
| - | + | ||
Revision as of 03:31, 17 February 2008
- 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.
- 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?, How do I detect outliers?, and How do I deal with outliers?.
 |  |
