Skewness and Kurtosis
Skewness and kurtosis are statistical measures used to describe the shape of a distribution.
Skewness
- Skewness is a measure of symmetry, or the lack of symmetry, in a distribution or dataset. It indicates the degree to which the data deviates from the normal distribution in terms of asymmetry.
- A distribution is symmetric if it looks the same to the left and right of the center point. Positive skewness indicates that the tail on the right side of the distribution is longer or fatter than the left side, while negative skewness indicates the opposite.
- Skewness is used to measure the level of asymmetry in a graph and denotes the horizontal pull on the data, showing how spread out the data is.
Kurtosis
- Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. It describes the shape of the distribution in terms of the presence of outliers.
- Data sets with high kurtosis tend to have heavy tails, or outliers, while data sets with low kurtosis tend to have light tails, or lack of outliers. A normal distribution has a kurtosis of 3 and is called mesokurtic.
- Kurtosis is used to find the vertical pull or the peaks height and gives an idea about the shape of a frequency distribution. It captures the phenomenon of the peak of the curve and the tails of the curve.
In summary, skewness and kurtosis are important statistical measures that provide insights into the shape and behavior of a distribution, helping analysts and researchers understand the characteristics of their data.
