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Skewness: Deciphering the Symmetry of Distributions

Skewness, a measure of a distribution’s symmetry, is the standardised third moment by dividing it by the standard deviation cubed.

In the vast realm of statistics, understanding data distributions stands paramount. Skewness emerges as a pivotal measure among the myriad of tools at our disposal. Let’s embark on a journey to unravel the mysteries of skewness and its role in deciphering distribution symmetry.

Understanding Skewness


At its core, skewness quantifies the degree of asymmetry in a data distribution. When we talk about it, we’re essentially discussing how much a distribution deviates from perfect symmetry.

The Importance of Statistical Analysis

Skewness offers invaluable insights into the nature of data. Analysts can make informed decisions by identifying the direction and degree of skew, whether they’re forecasting stock market trends or conducting market research.

Skewness vs. Symmetry

Decoding Distribution Symmetry

Like the renowned bell-shaped normal distribution, a perfectly symmetric distribution has equal tails on both sides. However, real-world data often deviates from this ideal, leading to skewed distributions.

Differentiating Between Symmetric and Skewed Distributions

The mean, median, and mode all coincide in a symmetric distribution. Conversely, skewed distributions see a separation between these measures, with the direction of skewness indicating where the tail elongates.

Calculating Skewness

A Step-by-Step Guide

To calculate, we can use the formula:

\[ Skewness = \frac{n}{(n-1)(n-2)} \sum \left( \frac{x_i – \bar{x}}{s} \right)^3 \]


  • \( n \) is the number of data points
  • \( x_i \) represents individual data points
  • \( \bar{x} \) is the mean
  • \( s \) is the standard deviation

For instance, consider a dataset: {2, 3, 4, 5, 6}. Using the formula, we can actively compute its skewness.

Pearson’s Coefficient

Pearson’s coefficient of skewness provides another method to gauge distribution symmetry. It’s calculated as: \[ Pearson’s \ coefficient = \frac{3(\bar{x} – Median)}{s} \]

This coefficient offers a quick way to determine its direction and magnitude.

Interpreting Skewness Values

Positive vs. Negative

A positive skewness value indicates a distribution with a longer right tail, suggesting that the bulk of the data lies to the left. On the other hand, a negative skewness value points to a distribution with a longer left tail.

The Significance of Zero Skewness

A skewness value of zero doesn’t necessarily mean perfect symmetry. Instead, it signifies that the distribution’s tails are balanced, even if they aren’t mirror images of each other.

Real-world Applications

In Financial Analysis

In the financial sector, skewness helps analysts predict stock returns. For instance, a positively skewed stock return distribution might indicate higher potential for large gains, albeit with occasional significant losses.

Market Research

Market researchers employ skewness to understand consumer behavior. For example, a negatively skewed distribution of product reviews might indicate that a product is generally well-received but has occasional strong detractors.


  1. How to tell if a distribution is symmetric or skewed? Examine the distribution’s histogram or frequency curve. If it appears balanced, it’s likely symmetric. However, if one tail seems elongated, the distribution is skewed.
  2. What is symmetric skewness? It’s a bit of a misnomer. Symmetric distributions have zero skewness, meaning they don’t favor one direction over the other.
  3. Is the histogram uniform, symmetric, or skewed? A uniform histogram has equal frequencies across intervals; meanwhile, a symmetric one mirrors around its center, and a skewed histogram shows an elongation on one side.


As we wrap up our exploration, it’s evident that it plays an indispensable role in understanding data distributions. By actively employing skewness in data analysis, we can glean deeper insights, make more informed decisions, and harness statistics’ power.

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