Skewness is a statistical measure of the asymmetry of a distribution. It is used to determine how much a distribution deviates from a normal distribution, which has a skewness of zero. A distribution can be positively skewed, negatively skewed, or have zero skewness.

• Positively skewed: A distribution is positively skewed when the tail of the distribution is longer on the right side, and the majority of the values are on the left side of the distribution.

• Negatively skewed: A distribution is negatively skewed when the tail of the distribution is longer on the left side, and the majority of the values are on the right side of the distribution.

• Zero skewness: A distribution has zero skewness when it is perfectly symmetrical.

Skewness is a descriptive statistic that can be used in conjunction with other measures, such as the histogram and the normal quantile plot, to characterize the data or distribution. It is also used to verify whether data is appropriate for a certain statistical procedure.

Skewed data can create an asymmetrical, skewed curve on a graph. Many financial models assume a normal distribution, so skewed data can increase the risk of turning up a data point of high skewness in a skewed distribution. If a statistical procedure requires a normal distribution and the data is skewed, there are several options, including using a different model or transforming the data.

In summary, skewness is a measure of the asymmetry of a distribution, and it can be used to determine how much a distribution deviates from a normal distribution. A distribution can be positively skewed, negatively skewed, or have zero skewness. Skewed data can create an asymmetrical, skewed curve on a graph, and it can increase the risk of turning up a data point of high skewness in a skewed distribution.