A great term in the field of statistics, which you can add to your dictionary, is **skewness**. It is the most commonly used tool to measure asymmetry. However, in order to fully understand it, basic knowledge of the *measures of central tendency* is vital.

Now, let’s focus on **skewness**. What is shown below, is the formula to calculate it.

Almost always, you will use software that performs the calculation for you. So, in this tutorial, we will not get into the computation, but rather the meaning of **skewness**.

**What's the Meaning of Skewness?**

So, **skewness** indicates whether the observations in a data set are concentrated on one side. **Skewness** can be confusing at the beginning, so an example will be useful.

In the picture below, we have three data sets and their respective frequency distributions. We have also calculated the **means**, **medians** and **modes**.

**Positive Skew**

The first data set has a **mean** of 2.79 and a **median** of 2. Hence, the **mean** is bigger than the **median**. We say that this is a **positive** or **right** **skew**. From the graph below, you can clearly see that the data points are concentrated on the left side.

**Important:** The direction of the **skew** is counterintuitive. It does not depend on which side the line is leaning to, but rather to which side its tail is leaning to. So, **right** **skewness** means that the ** outliers** are to the right.

It is interesting to see the measures of central tendency incorporated in the graph. You can tell that we have **right skewness **when:

- the
**mean**is bigger than the**median** - the
**mode**is the value with the highest visual representation.

**Zero Skew**

In the second graph, we have plotted a data set that has an equal **mean**, **median** and **mode**. The frequency of occurrence is completely symmetrical and we call this a **zero** or **no skew**.

Most often, you will hear people say that the distribution is *symmetrical*.

**Negative Skew**

For the third data set, we have a **mean** of 4.9, a **median** of 5 and a **mode** of 6. As the **mean** is lower than the **median**, we say that there is a **negative** or **left skew**.

Once again, the highest point is defined by the **mode**. And as you might guess, it is called a **left skew**, because the ** outliers** are to the left.

**Why Skewness is Important**

**Skewness** tells us a lot about where the data is situated.

In fact, the **mean**, **median** and **mode** should be used together to get a good understanding of the dataset. Measures of asymmetry like **skewness** are the link between central tendency measures and probability theory. This ultimately allows us to get a more complete understanding of the data we are working with.

Well, this is how we measure asymmetry. However, is there a way to measure something else? Like variability? Find out in our next tutorial.

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**Next ****Tutorial: **Measures of Variability: Variance, Standard Deviation and Coefficient of Variation