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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.

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. positive skew: The right tail is longer; the mass of the distribution is concentrated on the left of the figure. The distribution is said to be right-skewed.
2. negative skew: The left tail is longer; the mass of the distribution is concentrated on the right of the figure. The distribution is said to be left-skewed.

Skewness, the third standardized moment, is written as γ₁ and defined as

where μ₃ is the third moment about the mean and σ is the standard deviation. Equivalently, skewness can be defined as the ratio of the third cumulant κ₃ and the third power of the square root of the second cumulant κ₂:

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

where x_i is the i^th value, is the sample mean, m₃ is the sample third central moment, and m₂ is the sample variance.

Given samples from a population, the equation for the sample skewness g₁ above is a biased estimator of the population skewness. The usual estimator of skewness is

where k₃ is the unique symmetric unbiased estimator of the third cumulant and k₂ is the symmetric unbiased estimator of the second cumulant. Unfortunately G₁ 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.

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

• 3 (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.

• Skewness risk
• Kurtosis risk
• shape parameters