When the mean is less than the median in a dataset, we say that the distribution of the data is **left skewed**.

This means there is a “tail” on the left side of the distribution:

**Note**: Sometimes a left skewed distribution is also referred to as a *negatively skewed distribution*.

In a left skewed distribution, the mean is less than the median:

**What Causes the Mean to be Less than the Median?**

A distribution is typically left skewed when it is uncommon for a variable to take on a small value and much more common for a variable to take on values concentrated around a larger value.

One real-life example of a left skewed distribution would be exam scores among students.

Most students might score between 70 and 90 on a particular exam and it’s extremely uncommon for many students to score near a zero.

When we create a histogram to visualize the distribution of exam scores for some class, it will naturally be left skewed:

The mean is naturally less than the median because the high frequency of values on the right side of the distribution causes the median value to be larger.

As a simple example, suppose we have the following dataset that contains the exam scores of 20 students in a class:

**Dataset:** 24, 45, 56, 71, 78, 80, 81, 81, 82, 83, 84, 85, 85, 89, 91, 91, 92, 93, 96, 97

Here are the mean and median values of this dataset:

**Mean**: 79.2**Median**: 83.5

The mean value is dragged lower by the students who scored very low while the median value is located at the “middle” value of scores, which is 83.5.

If we plot this distribution, it would be a left skewed histogram with most of the values concentrated on the right side of the histogram.

**Additional Resources**

The following tutorials provide additional information about skewed distributions:

Left Skewed vs. Right Skewed Distributions

5 Examples of Negatively Skewed Distributions

5 Examples of Positively Skewed Distributions