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The median is a statistical measure that represents the middle point of a data set. When you list all your numbers in order from the smallest to the largest, the median is the number that appears right in the middle. It's like cutting a line of numbers in half.

Here's a simple way to find the median:

• Arrange your numbers in order.
• For an odd number of data points, the median is the middle number.
• For an even number of data points, it’s the average of the two middle numbers.

This is why the median is especially useful in real estate prices, as seen in the example with the median house price in 2014 at $282,800. It gives us a central point that isn't swayed by very high or very low values in the data set.

The mean, often referred to as the average, is another measure of central tendency. To find the mean, you add together all the individual values and then divide by the total number of values. For example, if we consider house prices, we would sum up the prices of all the houses and then divide by how many houses there are.

Here’s how you calculate it:

• Add up all the numbers.
• Divide the sum by the count of those numbers.

The mean can be deceptive because it is very sensitive to outliers. For instance, just one extremely expensive home could increase the mean significantly, as highlighted in the given data with a mean price of $345,800, much higher than the median. This makes it less representative of the typical prices most people might encounter.

Outliers are data points that differ significantly from other observations. In a data set like house prices, an outlier would be a property that is priced much higher or lower than the rest. These high or low priced houses can skew the average, or mean, but have no such impact on the median.

Here's what to know about outliers:

• They can disproportionately affect the mean, pulling it upward or downward.
• They do not affect the median, making it a more robust measure in such situations.

In real estate, a few luxurious homes with high price tags can significantly increase the mean. However, they don't change the median, which remains a more reliable measure of the typical property value. Understanding outliers is critical when interpreting data, as they can provide insights into the range and distribution of the data set.