About kurtosis and skewness

This is an article about kurtosis and skewness.

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Today we will learn about kurtosis and skewness.

Kurtosis and skewness are statistical measures used to describe the shape of a probability distribution.

Below is information about kurtosis and skewness.

Skewness

Skewness is an indicator of the skewness of a probability distribution and measures the degree of asymmetry of the distribution.

The closer the skewness is to 0, the closer the distribution is to symmetry. If it is greater than 0, it indicates a left-skewed asymmetric distribution with a long tail to the right. If it is less than 0, it indicates a right-skewed asymmetric distribution with a long tail to the left.

Skewness shows how the data is distributed around the mean.

Kurtosis

Kurtosis is an indicator of the sharpness of a probability distribution and measures the sharpness of the tails of the distribution.

A kurtosis close to 3 is similar to a normal distribution, a value less than 3 (negative kurtosis) indicates a distribution with flatter tails, and a kurtosis greater than 3 (positive kurtosis) indicates a distribution with sharper tails.

Kurtosis refers to the weight of the tails or the loading of the overlap, and shows how extreme values ​​are distributed.

uses

Skewness and kurtosis are mainly used to determine whether data follows a normal distribution and whether it is asymmetric or sharp.

Conclusion

These statistical measures help you understand the shape and distribution of data to select appropriate statistical analysis methods or better understand the characteristics of the data.

Kurtosis and skewness are used as important indicators to understand the distribution characteristics of data and confirm the assumption of normality.

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