# How to Interpret Data where Mean is Greater than Median

When the mean is greater than the median in a dataset, we say that the distribution of the data is right skewed.

This means there is a “tail” on the right side of the distribution: Note: Sometimes a right skewed distribution is also referred to as a positively skewed distribution.

In a right skewed distribution, the mean is greater than the median: ## What Causes the Mean to be Greater than the Median?

A distribution is typically right skewed when there is a limit on the minimum possible value but no limit on the maximum possible value.

One real-life example of a right skewed distribution is the distribution of income in a country.

The minimum income that a person could earn is zero dollars but there is no maximum income that a person could earn.

When we create a histogram to visualize the distribution of income, it will naturally be right skewed: The mean is naturally greater than the median because the large values on the right “tail” of the distribution will greatly inflate the value of the mean.

As a simple example, suppose we have the following dataset that contains the income of 10 individuals:

Dataset 1: \$30k, \$35k, \$35k, \$40k, \$50k, \$55k, \$55k, \$70k, \$90k, \$110k

Here are the mean and median values of this dataset:

• Mean: \$57k
• Median: \$52.5k

Now suppose we have another dataset that contains the exact same incomes except the last value is now \$2.5 million:

Dataset 2: \$30k, \$35k, \$35k, \$40k, \$50k, \$55k, \$55k, \$70k, \$90k, \$2.5 million

Here are the mean and median values of this dataset:

• Mean: \$296k
• Median: \$52.5k

This last outlier value causes the mean income to increase significantly.

And if we plot this distribution, it would be a right skewed histogram with the \$2.5 million value located on the right “tail” of the histogram.