Why Mean and Median Are Both Important in Statistical Data
In statistics, the average and the median are two different representations of the center of a data set and can often give two very different stories about the data, especially when the data set contains outliers.
The mean, also referred to by statisticians as the average, is the most common statistic used to measure the center of a numerical data set. The mean is the sum of all the values in the data set divided by the number of values in the data set. The mean of the entire population is called the population mean, and the mean of a sample is called the sample mean.
The mean may not be a fair representation of the data, because the average is easily influenced by outliers (very small or large values in the data set that are not typical).
The median is another way to measure the center of a numerical data set. A statistical median is much like the median of an interstate highway. On many highways, the median is the middle, and an equal number of lanes lay on either side of it. In a numerical data set, the median is the point at which there are an equal number of data points whose values lie above and below the median value. Thus, the median is truly the middle of the data set.
The next time you hear an average reported, look to see whether the median is also reported. If not, ask for it!