Skewness – Definition, Types & How To Calculate It

Skewness, a fundamental term in the realm of statistics, serves as a crucial measure of the asymmetry of a probability distribution. It provides significant insights into the shape of a dataset, specifically how it differs from a symmetric normal distribution. As we delve deeper into this statistical concept, we will explore its importance in data analysis, its calculation, and the types of skewness. Understanding skewness is an essential step towards comprehensively interpreting statistical data.

Definition: Skewness

Skewness is a distortion, bend, or asymmetry that moves away from a data set’s normal distribution or the symmetrical bell curve. This deviation may shift to the left or right of the symmetrical bell curve. Generally, the skew of a normal distribution is zero because it’s symmetrical on either side.

Types of skewness

Skewness impacts the string or tail of data points away from the median. The three types of skewness include positive, zero, and negative, as explained below:

Zero skew

Normal distributions usually have zero skew values because the left and right sides are symmetrical. Other symmetrical distributions with a zero skew include some bimodal (two-peak) and uniform distributions.

The easiest way to know if variables are symmetrical is by using a histogram. If the distributions on both sides of the histogram are balanced, they have a zero skew. Additionally, a zero-skew distribution has an equal nasty and median.

Zero skews: nasty = median

Note that real-world data rarely have exact equal nasty and median. However, if the median and nasty are close to being asymmetrical, they have usually considered zero skews, for instance, a nasty of 261.5g and a median of 285g.

Right skew (positive skew)

A right-skewed distribution, also called a positive skew, has a longer tail on its right side. The nasty of right-skewed data sets is greater than the corresponding median. This is because extreme values impact the nasty more than the median.

Left skew (negative skew)

A left-skewed distribution or a negative skew has a longer tail on its left side. Here, the nasty is almost always lesser than the median.

How to calculate skewness

Pearson’s formula is the standard formula used to calculate skewness. This formula uses the theory that the nasty and median in a skewed distribution are unequal, and it’s explained as follows:

This formula is essential for understanding the standard deviation values that separate the nasty and median. In real-life observations, Pearson’s median skewness rarely has an exact score of zero. If your data results in a zero-skew score, consider it a zero-skew.

No standard convention counts as “close enough” to zero. However, some scholars concur that values of -0.4 and 0.4 are a reasonable cutoff for larger samples.

Skewness in data

Most statistical procedures assume that residuals or variables are normally distributed. You can use a skew to check whether your variables are appropriate for your statistical approach. You have the following three choices if you want a normal distribution, but you have skewed data:

• Find other methods: Like non-parametric tests that don’t assume a normal distribution.
• Ignore the skew: Tests like ANOVA, linear regression, and t-tests aren’t sensitive to skew.
• Transforming the skew: By applying the same function to the other observations of the variables.

Transformations based on the skew type

You can use the following transformations based on the skew type:

*The term “reflect” means you should take the greatest observation, K, before subtracting each observation from K+1. It would be best if you remembered that this observation changes the direction of variables and any relationship it has with other variables; such as, negative relationships become positive.

Example: Right-skewed variable transformation

You decide to perform a linear regression to predict the yearly number of sunspots. However, the results show that the data is not normally distributed. You discern that the sunspots observed yearly are right-skewed; hence, you can address this issue through a transformation.

You have another option, ignoring the skew because linear regression is not sensitive to skew. The first step is using a square root transformation. If this change isn’t enough, move to the next transformation step, as the table below shows:

The next step is placing your results in a histogram. If the skew is close to zero, replace the number of sunspots observed yearly with the transformed variables. Since the skew is near zero, likely, the linear regression is now normally distributed.