# What is Skewness and how data get affected

In this blog, we would discuss What is Skewness and how data get affected. Skewness is a measure of the asymmetry of a data set. It is calculated as the difference between the mean and median of a data set, divided by the standard deviation. Skewness can be either positive or negative, depending on which side of the data the mean is located. Positively skewed data sets are more common than negatively skewed data sets. Skewness is important because it can affect the results of statistical tests.

**For example**, if a distribution is skewed, the results of a t-test may not be valid. There are ways to correct for skewness, but it is best to avoid it if possible. The best way to avoid skewness is to use a random sample.

## What is Skewness?

Skewness is a statistical measure that describes the asymmetry of a data set. It is calculated as the difference between the mean and median of a data set, divided by the standard deviation. A data set is symmetrical if the mean and median are equal. If the mean is greater than the median, the data set is said to be positively skewed.

If the mean is less than the median, the data set is said to be negatively skewed. Most data sets are not perfectly symmetrical, so skewness is a useful measure to quantify the degree of asymmetry. Skewness can be either positive or negative, depending on which side of the data the mean is located.

Positively skewed data sets are more common than negatively skewed data sets. This is because there are more possible ways for a data set to be positively skewed than negatively skewed. For example, a data set can be positively skewed if it has a long tail of high values. It can also be positively skewed if it has a short tail of low values.

Negatively skewed data sets are less common than positively skewed data sets. This is because there are fewer ways for a data set to be negatively skewed than positively skewed. For example, a data set can be negatively skewed if it has a long tail of low values. It can also be negatively skewed if it has a short tail of high values.

## How does the skewness of data get affected?

The skewness of a data set can be affected by outliers. Outliers are data points that are far from the rest of the data. They can skew the data to the right or the left, depending on their value.

The skewness of a data set can also be affected by the presence of multiple modes. A mode is a value that occurs most often in a data set. If a data set has two modes, it is bimodal. If a data set has three modes, it is trimodal.

The skewness of a data set is affected by the mean, median, and mode. The mean is the average of the data. The median is the middle value of the data. The mode is the value that occurs most often in the data.

The skewness of a data set is affected by the distribution of the data. The distribution of the data is the way the data are spread out. The data can be spread out evenly, or they can be spread out unevenly.

The skewness of a data set is affected by the standard deviation of the data. The standard deviation is a measure of the spread of the data. The larger the standard deviation, the more spread out the data are.

The skewness of a data set is affected by the shape of the data. The shape of the data is the way the data are distributed. The data can be distributed evenly, or they can be distributed unevenly.

## Why is skewness important?

First, it can impact the results of statistical tests. For example, the t-test assumes that the data is normally distributed, but if the data is skewed, the results of the t-test may not be accurate. Additionally, skewness can impact the interpretability of data. For instance, if you’re looking at a histogram of data, a positively skewed data set will have a longer tail to the right, while a negatively skewed data set will have a longer tail to the left.

Skewness is important because it is a measure of how “off” a distribution is from being perfectly symmetrical. A positive skew indicates that the distribution is shifted to the right, while negative skew indicates that the distribution is shifted to the left.

Some fields, such as finance, place great importance on skewness because it can be used to predict future events. For example, a negatively skewed distribution of stock prices might indicate that a crash is coming, while a positively skewed distribution might indicate that prices are about to skyrocket.

Other fields, such as medicine, use skewness to help diagnose conditions. For example, a skewed distribution of blood pressure readings might indicate that a person has hypertension. In general, skewness is important because it allows us to better understand the data that we are working with. It can help us to make predictions and to diagnose problems.

Also, read – What are Naive Bayes and its working

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