# Difference Between Dispersion and Skewness

In statistics, measures of central tendency such as mean, median, and mode provide information on the central value of a dataset. However, these measures do not provide complete information on the distribution of the dataset. Two important statistical measures that complement measures of central tendency are dispersion and skewness.

## What is Dispersion?

Dispersion is a statistical term that measures the spread or variability of a dataset. It is a measure of how much the data points in a dataset deviate from the central value or mean. In other words, it measures how much the data points are scattered around the central value. Dispersion can be measured using several statistical measures, including range, variance, standard deviation, and interquartile range.

Table 1: Dispersion Measures

Measure Formula
Range Maximum value - Minimum value
Variance (Σ(xi - μ)^2) / n
Standard Deviation √(Σ(xi - μ)^2 / n)
Interquartile Range Q3 - Q1

Where:

• xi is the ith data point
• μ is the mean
• n is the number of data points
• Q1 is the first quartile (25th percentile)
• Q3 is the third quartile (75th percentile)

## What is Skewness?

Skewness is a statistical term that measures the asymmetry of a dataset. It measures how much a dataset deviates from a normal distribution or symmetrical bell curve. A dataset can be skewed to the left (negative skewness) or skewed to the right (positive skewness). A dataset that is symmetrical has zero skewness.

Table 2: Types of Skewness

Type Skewness
Negative Skewness Mean < Median
Positive Skewness Mean > Median
Zero Skewness Mean = Median

## What is the Difference between Dispersion and Skewness?

Dispersion and skewness are both important statistical measures that provide information about the distribution of a dataset. However, there are some key differences between them.

Dispersion measures the spread or variability of a dataset, while skewness measures the asymmetry of a dataset. Dispersion can be measured using a variety of measures such as range, variance, standard deviation, and interquartile range. On the other hand, skewness is typically measured using the mean and median.

Another difference between dispersion and skewness is what they reveal about the data. Dispersion provides information on the variability and reliability of the data, while skewness provides information on the shape of the distribution. Specifically, skewness measures the extent to which a distribution is skewed to the left or right.

Finally, it is worth noting that dispersion and skewness can be influenced by outliers or extreme values in the dataset. This is particularly important to keep in mind when interpreting the results of these measures. Outliers can significantly increase the dispersion and skewness, which may not accurately represent the variability and shape of the rest of the data.

While dispersion and skewness are both important measures for understanding the distribution of a dataset, they differ in what they reveal about the data and how they are measured. Dispersion measures the spread or variability of a dataset, while skewness measures the asymmetry of a dataset.

## Relationship between Dispersion and Skewness

Dispersion and skewness are related in that a dataset with high dispersion can have high skewness. For example, a dataset with a large range or standard deviation can be skewed, indicating that the data points are not evenly distributed around the central value. Similarly, a dataset with low dispersion can have low skewness, indicating that the data points are more evenly distributed around the central value.

Example of Dispersion and Skewness

Consider the following dataset:

{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}

The mean of this dataset is 5.5, and the median is 5.5. The range is 9 (10 - 1), the variance is 8.25, and the standard deviation is 2.87. The interquartile range is 4 (7 - 3).

The skewness of this dataset is zero, indicating that it is symmetrical. However, if we modify the dataset to:

{1, 2, 3, 4, 5, 6, 7, 8, 9, 100}

The mean of this dataset is 16.5, and the median is 5.5. The range is 99, the variance is 933.61, and the standard deviation is 30.55. The interquartile range is 4 (7 - 3).

The skewness of this dataset is positive, indicating that it is skewed to the right. This is because the data point 100 is an outlier that pulls the mean to the right.

## Comparison Table

To summarize the differences between dispersion and skewness, we can use the following comparison table:

Dispersion Skewness
Measures the spread or variability of a dataset Measures the asymmetry of a dataset
Can be measured using range, variance, standard deviation, and interquartile range Can be measured using mean and median
Provides information on the variability and reliability of the data Provides information on the shape of the distribution
Can be influenced by outliers or extreme values in the dataset Can be influenced by outliers or extreme values in the dataset
High dispersion can indicate high skewness Symmetrical datasets have zero skewness

Statistics is the study of numerical data, and there are several key measures that are used to describe the distribution of a dataset. Two important measures are dispersion and skewness. While both provide important information about the distribution of a dataset, they are different concepts and serve different purposes.

What Is the Difference Between Dispersion and Skewness?

Dispersion measures the variability or spread of a dataset. It is a measure of how spread out the data points are from the center of the distribution. Dispersion can be measured using a variety of measures such as range, variance, standard deviation, and interquartile range.

On the other hand, skewness measures the symmetry or asymmetry of a dataset. It is a measure of how skewed the distribution is to the left or right. Skewness is typically measured using the mean and median.

In summary, dispersion measures how spread out the data points are from the center of the distribution, while skewness measures how symmetric or asymmetric the distribution is.

What Is the Difference Between Dispersion and Distribution?

While dispersion measures the variability or spread of a dataset, distribution refers to the pattern of data points within the dataset. The distribution describes how the data points are arranged or distributed across the range of possible values.

There are several common types of distributions, including normal distributions, uniform distributions, and skewed distributions. Dispersion and distribution are related in that dispersion measures the variability of the distribution, but they are distinct concepts.

How Does Skewness Differ From Dispersion and Kurtosis?

Skewness and dispersion are measures of the shape of a distribution, while kurtosis measures the peakedness of a distribution. Kurtosis is a measure of the height and sharpness of the peak in the distribution. A distribution with high kurtosis has a sharp peak, while a distribution with low kurtosis has a flatter peak.

In summary, while skewness and dispersion measure the shape of a distribution, kurtosis measures the peakedness of the distribution.

What Is the Difference Between Skewness and Kurtosis?

While both skewness and kurtosis measure the shape of a distribution, they measure different aspects of shape. Skewness measures the symmetry or asymmetry of the distribution, while kurtosis measures the peakedness or flatness of the distribution.

A distribution with positive skewness is skewed to the right, while a distribution with negative skewness is skewed to the left. A distribution with high kurtosis has a sharp peak, while a distribution with low kurtosis has a flatter peak.

In summary, while both skewness and kurtosis measure the shape of a distribution, they measure different aspects of shape. Skewness measures the symmetry or asymmetry of the distribution, while kurtosis measures the peakedness or flatness of the distribution.

What Is the Difference Between Correlation and Skewness?

Correlation measures the strength and direction of the relationship between two variables. It is a measure of how closely two variables are related to each other. Skewness, on the other hand, measures the symmetry or asymmetry of a single variable.

While both correlation and skewness are measures of statistical association, they measure different aspects of association. Correlation measures the association between two variables, while skewness measures the asymmetry of a single variable.

What Is the Difference Between Variation and Skewness?

Variation measures the spread or variability of a dataset, while skewness measures the symmetry or asymmetry of a dataset. Variation can be measured using a variety of measures such as range, variance, standard deviation, and interquartile range.

In summary, while both variation and skewness measure aspects of the distribution of a dataset, they measure different aspects. Variation measures the spread or variability of the data, while skewness measures the symmetry or asymmetry of the data.

Conclusion

Dispersion and skewness are important statistical measures that provide valuable information about a dataset. Dispersion measures the spread or variability of a dataset, while skewness measures the asymmetry of a dataset. They complement measures of central tendency and can help identify outliers, assess the variability of a dataset, and determine the shape of the distribution. However, they can also be influenced by outliers or extreme values in the dataset, and may not provide a complete picture of the dataset.