Skewness is a statistical measure that quantifies the extent to which a probability distribution deviates from symmetry or the normal bell curve shape. It is a measure of the asymmetry of a distribution and indicates whether the data is skewed to the left (negatively skewed), skewed to the right (positively skewed), or is symmetrical (zero skewness).
In a positively skewed distribution, also known as right-skewed, the tail of the distribution is stretched towards the right, and the majority of the data tends to be concentrated on the left side. This indicates that there are more extreme values on the right side of the distribution. Conversely, a negatively skewed distribution, also known as left-skewed, is characterized by a tail stretched towards the left, with the majority of data concentrated on the right side.
The measure of skewness is computed using formulas that take into account the deviations of individual data points from the mean as well as the standard deviation. A positive skewness value indicates a longer and fatter right tail, while a negative skewness value indicates a longer and fatter left tail. A skewness value of zero suggests a perfectly symmetrical distribution.
Skewness is an important concept in statistics as it helps to understand the shape and characteristics of a dataset. It provides valuable insights into the distribution of data and aids in making data-driven decisions, particularly in fields such as finance, economics, and social sciences.
