Difference Between Skewness and Kurtosis

# Difference Between Skewness and Kurtosis

Vikram Singh
Assistant Manager - Content
Updated on Jun 14, 2024 15:24 IST

Skewness and Kurtosis are statistical measures used to describe the shape and characteristics of a distribution in statistics. Skewness is a measure of symmetry (or, more specifically, the lack of symmetry in the data set), which can be positive or negative. In contrast, Kurtosis measures the tailedness of a distribution (i.e., it quantifies the extremity of outliers in the distribution). This article will briefly discuss Skewness and Kurtosis and their similarities and differences.

We always look for skewness and kurtosis while analyzing the data or doing Exploratory Data Analysis. Both measures give information about the shape of the distribution, but they capture the different aspects of the data. Skewness measures the degree of asymmetry in the distribution, while kurtosis measures the degree of peakedness or flatness.

This article will describe what skewness and kurtosis are and the differences between them, and later in the article, we will discuss how to calculate skewness and kurtosis using Python.

So, let’s start the article by giving the difference between skewness and kurtosis.

Table of Content

## What isSkewness?

Skewness defines the shape of the distribution. Usually, we get a lot of asymmetric distributions, and these distributions have unevenly spread data. There are two types of skewness – positive or right-skewed and negative or left-skewed.

Positive skewness is when the distribution takes place so that we get a long tail towards the right side of the graph. This is called a right-skewed graph,

In this distribution, the mean is greater than the median, which is greater than the mode. That is, we get mean > median > mode.

Negative skewness is when the distribution takes place so that we get a long tail towards the left side of the graph. This is called a left-skewed graph.

## What isKurtosis?

Kurtosis is a statistical measure that describes the degree of peakedness or flatness of a distribution. It measures the shape of the distribution, specifically the height and sharpness of the central peak, relative to that of normal distribution. It is the fourth moment of statistics.

The term “Kurtosis” comes from the Greek word “Kurtos”, which means curved.

Kurtosis is useful to identify the potential outliers in a dataset, as distributions with high kurtosis have more extreme values than normal distributions.

There are three types of Kurtosis: Mesokurtic, Leptokurtic, and Platykurtic.

#### Types of Kurtosis:

Mesokurtic: This is a type of distribution in which there is symmetry. This means that both the extreme ends of the graph are similar. This is the same as the normal distribution.

Leptokurtic: This distribution has a greater kurtosis than the mesokurtic, which has longer tails. This indicates that a more significant percentage of data is present near the tail, which causes the tail to get longer.

Platykurtic: This distribution has lower kurtosis than the mesokurtic. That is, it has shorter tails. This means there is less data in the tail portion, which makes the tail flatter.

## Key Differences Between Skewness and Kurtosis

• Skewness measures the degree of asymmetry of the distribution, while Kurtosis measures the degree of peakedness and flatness of a distribution.
• Skewness is the third measure of moments, while kurtosis is the fourth measure of moments.
• The value of both Skewness and Kurtosis ranges from -infinity to +infinity.
• Both zero skewness and zero kurtosis represent perfect symmetry and perfect normality.
• Skewness can affect the center of the distribution, while kurtosis can affect the distribution’s tails.
• Both Skewness and Kurtosis describe the shape of distributions.

## Conclusion

This article briefly covered what skewness and kurtosis are, their types, and their differences. Hope you will like the article.

Happy Learning!!

Difference between Correlation and Regression
Correlation measures the degree of relationship between two variables while regression is about how one variable affects the other. In this article, we will briefly discuss the difference between correlation...read more
Measures of Central Tendency: Mean, Median and Mode
When we have the dataset having ample records (like passenger traveling through of airplane, weight, and score of all students in a university, share prices) in it and...read more
Statistics Interview Questions for Data Scientists
In this article, Statistics Interview Questions for Data Scientists are listed. It starts with defining Statistics and ends with describing Empirical Rule.
Skewness in Statistics – Overview, Concepts, Types, Measurements and Importance
Imagine a seesaw—perfectly balanced, right? That's how data can sometimes be—nice and even on both sides. But what if all the kids pile on one side? That's kind of like...read more
Difference between Median and Average
Average and median are two basic terms that are used in statistics very often. Median is the middle value in a set, whereas average is an arithmetic mean of set...read more
68-95-99.7 Rule: Definition and Implementation
68-95-99.7 Rule or the empirical rule is based on mean and standard deviation. It is a shorthand for remembering percentage of values lying within interval estimate in the normal distribution.

## FAQs

What is Skewness?

Skewness defines the shape of the distribution. Usually, we get a lot of asymmetric distributions, and these distributions have unevenly spread data. There are two types of skewness – positive or right-skewed and negative or left-skewed.

What is Kurtosis?

Kurtosis is a statistical measure that describes the degree of peakedness or flatness of a distribution. It measures the shape of the distribution, specifically the height and sharpness of the central peak, relative to that of normal distribution. It is the fourth moment of statistics.

What are the key differences between skewness and kurtosis?

Skewness measures the degree of asymmetry of the distribution, while Kurtosis measures the degree of peakedness and flatness of a distribution. Skewness is the third measure of moments, while kurtosis is the fourth measure of moments. The value of both Skewness and Kurtosis ranges from -infinity to +infinity. Both zero skewness and zero kurtosis represent perfect symmetry and perfect normality. Skewness can affect the center of the distribution, while kurtosis can affect the distribution's tails. Both Skewness and Kurtosis describe the shape of distributions.

What are the different types of Skewness?

There are two types of skewness – positive or right-skewed and negative or left-skewed.

Positive Skewness is when the distribution takes place so that we get a long tail towards the right side of the graph. This is called a right-skewed graph,

Negative Skewness is when the distribution takes place so that we get a long tail towards the left side of the graph. This is called a left-skewed graph.

What are the different types of Kurtosis?

Mesokurtic: This is a type of distribution in which there is symmetry. This means that both the extreme ends of the graph are similar. This is the same as the normal distribution.

Leptokurtic: This distribution has a greater kurtosis than the mesokurtic, which has longer tails. This indicates that a more significant percentage of data is present near the tail, which causes the tail to get longer.

Platykurtic: This distribution has lower kurtosis than the mesokurtic. That is it has shorter tails. This means there is fewer data in the tail portion, which makes the tail flatter.