Example of experimental data with non-zero skewness (gravitropic response of wheat coleoptiles, 1,790)

In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable.

Introduction

Consider the distribution in the figure. The bars on the right side of the distribution taper differently than the bars on the left side. These tapering sides are called tails (or snakes), and they provide a visual means for determining which of the two kinds of skewness a distribution has:

1. negative skew: The left tail is longer; the mass of the distribution is concentrated on the right of the figure. It has a few relatively low values. The distribution is said to be left-skewed. In such a distribution, the mean is lower than median which in turn is lower than the mode (i.e.; mean < median < mode); in which case the skewness coefficient is lower than zero. Example: 1,1000,1001,1002,1003
   1. positive skew: The right tail is longer; the mass of the distribution is concentrated on the left of the figure. It has a few relatively high values. The distribution is said to be right-skewed. In such a distribution, the mean is greater than median which in turn is greater than the mode (i.e.; mean > median > mode); in which case the skewness coefficient is greater than zero. Example: 1,2,3,4,100

In a skewed (unbalanced, lopsided) distribution, the mean is farther out in the long tail than is the median. If there is no skewness or the distribution is symmetric like the bell-shaped normal curve then the mean = median = mode.

Definition

Skewness, the third standardized moment, is written as \gamma_1 and defined as

\gamma_1 = \frac{\mu_3}{\sigma^3}, \!

where \mu_3 is the third moment about the mean and \sigma is the standard deviation. Equivalently, skewness can be defined as the ratio of the third cumulant \kappa_3 and the third power of the square root of the second cumulant \kappa_2:

\gamma_1 = \frac{\kappa_3}{\kappa_2^{3/2}}. \!

This is analogous to the definition of kurtosis, which is expressed as the fourth cumulant divided by the fourth power of the square root of the second cumulant.

For a sample of n values the sample skewness is

g_1 = \frac{m_3}{m_2^{3/2}} = \frac{\tfrac{1}{n} \sum_{i=1}^n (x_i - \overline{x})^3}{\left(\tfrac{1}{n} \sum_{i=1}^n (x_i - \overline{x})^2\right)^{3/2}}, \!

where x_i is the i^th value, \bar{x} is the sample mean, m_3 is the sample third central moment, and m_2 is the sample variance.

Given samples from a population, the equation for the sample skewness g_1 above is a biased estimator of the population skewness. The usual estimator of skewness is

G_1 = \frac{k_3}{k_2^{3/2}} = \frac{\sqrt{n\,(n-1)}}{n-2}\; g_1, \!

where k_3 is the unique symmetric unbiased estimator of the third cumulant and k_2 is the symmetric unbiased estimator of the second cumulant. Unfortunately G_1 is, nevertheless, generally biased. Its expected value can even have the opposite sign from the true skewness.

The skewness of a random variable X is sometimes denoted Skew[X]. If Y is the sum of n independent random variables, all with the same distribution as X, then it can be shown that Skew[Y] = Skew[X] / ?n.

Skewness has benefits in many areas. Many simplistic models assume normal distribution i.e. data is symmetric about the mean. The normal distribution has a skewness of zero. But in reality, data points are not perfectly symmetric. So, an understanding of the skewness of the dataset indicates whether deviations from the mean are going to be positive or negative.

Pearson skewness coefficients

Karl Pearson suggested two simpler calculations as a measure of skewness:

• (mean - mode) / standard deviation
• 3 (mean - median) / standard deviation

There is no guarantee that these will be the same sign as each other or as the ordinary definition of skewness.

See also

• Skewness risk
• Kurtosis risk
• Shape parameters
• Skew normal distribution

External links

• An Asymmetry Coefficient for Multivariate Distributions by Michel Petitjean

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