Editing Skewness (section)

== Relationship of mean and median ==
The skewness is not directly related to the relationship between the mean and median: a distribution with negative skew can have its mean greater than or less than the median, and likewise for positive skew.<ref name="von Hippel 2005">{{cite journal |last=von Hippel |first=Paul T. |year=2005 |title=Mean, Median, and Skew: Correcting a Textbook Rule |url=http://www.amstat.org/publications/jse/v13n2/vonhippel.html |url-status=dead |journal=Journal of Statistics Education |volume=13 |issue=2 |archive-url=https://web.archive.org/web/20160220181456/http://www.amstat.org/publications/jse/v13n2/vonhippel.html |archive-date=2016-02-20}}</ref>
[[File:Relationship between mean and median under different skewness.png|thumb|434x434px|A general relationship of mean and median under differently skewed unimodal distribution.]]
In the older notion of [[nonparametric skew]], defined as <math>(\mu - \nu)/\sigma,</math> where <math>\mu</math> is the [[mean]], <math>\nu</math> is the [[median]], and <math>\sigma</math> is the [[standard deviation]], the skewness is defined in terms of this relationship: positive/right nonparametric skew means the mean is greater than (to the right of) the median, while negative/left nonparametric skew means the mean is less than (to the left of) the median. However, the modern definition of skewness and the traditional nonparametric definition do not always have the same sign: while they agree for some families of distributions, they differ in some of the cases, and conflating them is misleading.

If the distribution is [[Symmetric probability distribution|symmetric]], then the mean is equal to the median, and the distribution has zero skewness.<ref>{{cite web|title=1.3.5.11. Measures of Skewness and Kurtosis |url=http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm|publisher=NIST|access-date=18 March 2012}}</ref> If the distribution is both symmetric and [[Unimodal distribution|unimodal]], then the [[mean]] = [[median]] = [[Mode (statistics)|mode]]. This is the case of a coin toss or the series 1,2,3,4,... Note, however, that the converse is not true in general, i.e. zero skewness (defined below) does not imply that the mean is equal to the median.

A 2005 journal article points out:<ref name="von Hippel 2005"/><blockquote>Many textbooks teach a rule of thumb stating that the mean is right of the median under right skew, and left of the median under left skew. This rule fails with surprising frequency. It can fail in [[multimodal distribution]]s, or in distributions where one tail is [[Long tail|long]] but the other is [[Heavy-tailed distribution|heavy]]. Most commonly, though, the rule fails in discrete distributions where the areas to the left and right of the median are not equal. Such distributions not only contradict the textbook relationship between mean, median, and skew, they also contradict the textbook interpretation of the median.</blockquote>
[[File:Positive skewness with mean less than median.png|thumb|288x288px|Distribution of adult residents across US households]]
For example, in the distribution of adult residents across US households, the skew is to the right. However, since the majority of cases is less than or equal to the mode, which is also the median, the mean sits in the heavier left tail. As a result, the rule of thumb that the mean is right of the median under right skew failed.<ref name="von Hippel 2005" />