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Definition

skewness

Contributor(s): Stan Gibilisco

Skewness is asymmetry in a statistical distribution, in which the curve appears distorted or skewed either to the left or to the right. Skewness can be quantified to define the extent to which a distribution differs from a normal distribution.

In a normal distribution, the graph appears as a classical, symmetrical "bell-shaped curve." The mean, or average, and the mode, or maximum point on the curve, are equal.

  • In a perfect normal distribution (green solid curve in the illustration below), the tails on either side of the curve are exact mirror images of each other.
  • When a distribution is skewed to the left (red dashed curve), the tail on the curve's left-hand side is longer than the tail on the right-hand side, and the mean is less than the mode. This situation is also called negative skewness.
  • When a distribution is skewed to the right (blue dotted curve), the tail on the curve's right-hand side is longer than the tail on the left-hand side, and the mean is greater than the mode. This situation is also called positive skewness.

This was last updated in December 2012

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Opps, Got a question, if a census is skewed to one side, does that mean the census is biased?
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You got the skewness wrong way round! Left skew is positive skewness, right skew is negative skewness!
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No it's not; Right (Positive), Left (Negative).
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Can skewness of any data presentation be regarded as bias in any circumstance?
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