Difference Between Skewness and Kurtosis

What is Skewness?

Skewness is an important statistical technique that helps to determine the asymmetrical behavior of the frequency distribution, or more precisely, the lack of symmetry of tails both left and right of the frequency curve. A distribution or dataset is symmetric if it looks the same to the left and right of the center point.

Types of Skewness

The following figure describes the classification of skewness:

1. Symmetric Skewness: A perfect symmetric distribution is one in which frequency distribution is the same on the sides of the center point of the frequency curve. In this, Mean = Median = Mode. There is no skewness in a perfectly symmetrical distribution.

2. Asymmetric Skewness: A asymmetrical or skewed distribution is one in which the spread of the frequencies is different on both the sides of the center point or the frequency curve is more stretched towards one side or value of Mean. Median and Mode falls at different points.

• Positive Skewness: In this, the concentration of frequencies is more towards higher values of the variable i.e. the right tail is longer than the left tail.
• Negative Skewness: In this, the concentration of frequencies is more towards the lower values of the variable i.e. the left tail is longer than the right tail.

What is Kurtosis?

It is also a characteristic of the frequency distribution. It gives an idea about the shape of a frequency distribution. Basically, the measure of kurtosis is the extent to which a frequency distribution is peaked in comparison with a normal curve. It is the degree of peaked Ness of a distribution. 

Types of Kurtosis

The following figure describes the classification of kurtosis:

1. Leptokurtic: Leptokurtic is a curve having a high peak than the normal distribution. In this curve, there is too much concentration of items near the central value.
2. Mesokurtic: Mesokurtic is a curve having a normal peak than the normal curve. In this curve, there is equal distribution of items around the central value.
3. Platykurtic: Platykurtic is a curve having a low peak than the normal curve is called platykurtic. In this curve, there is less concentration of items around the central value.

Difference Between Skewness and Kurtosis

Below is the difference between Skewness and Kurtosis.

Conclusion

Understanding skewness and kurtosis is crucial for analyzing and interpreting data distributions. Skewness helps identify the asymmetry of a distribution, indicating whether data points are more concentrated on one side of the mean. On the other hand, kurtosis measures the "peakedness" or "tailedness" of a distribution. Both skewness and kurtosis provide valuable insights into the shape and characteristics of data, enabling more accurate data analysis and decision-making.

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