Mean and median are measures of central tendency in a data set. Mean and median both examine where, in a data set, numbers are likely to occur with the most frequency. However, depending upon the type of data set, one can be a better way than another of evaluating data and coming up with statistical results.

Getting the mean of something is the same as obtaining the average number in a data set. The sum of the numbers in the set is divided by the total of numbers in a set. Thus for example, a teacher might evaluate five test scores, all weighted equally, to determine a grade for a student.

Say the five test scores are 80, 85, 60, 90, and 100. These numbers are added together and their sum is 415. The teacher then divides 415 by 5 to get the mean score of 83. When using mean, the teacher figures the test scores result in a B grade in the class.

In a median measurement, the data is arranged from lowest to highest: 60, 80, 85, 90, and 100. The middle number in this set is the median. In our example, the median is 85, the third and middle number of the set. This varies slightly from the mean of 83. A teacher may wish to look at a median score, as it tends to rule out a low score like 60, a low D.

Mean scores tend to be affected by outliers, data that tends to be far removed from the central tendency of the number. Thus for example, if the 60 score becomes a 40 score, the mean continues to reduce, while the median, given 5 test scores remains the same. So in some cases, median can be a better measure of central tendencies that discounts the importance of numbers outside the data range.

On the other hand, in our example, the teacher might be simply interested in making sure all tests contribute to the grade. Thus an outlier score that affects the mean will result in a lower overall grade.