By now, everyone should know how to calculate mean, median and mode. They each give us a measure of Central Tendency (i.e. where the center of our data falls), but often give different answers. So how do we know when to use each? Here are some general rules:

1.  Mean is the most frequently used measure of central tendency and generally considered the best measure of it. However, there are some situations where either median or mode are preferred.
2. Median is the preferred measure of central tendency when:
   1.  There are a few extreme values in the distribution of the data. (NOTE: Remember that a single outlier can have a great effect on the mean).
   2. There are some missing or undetermined values in your data.
   3. There is an open ended distribution (For example, if you have a data field which measures number of children and your options are [latex]0[/latex], [latex]1[/latex], [latex]2[/latex], [latex]3[/latex], [latex]4[/latex], [latex]5[/latex] or “[latex]6[/latex] or more,” than the “[latex]6[/latex] or more field” is open ended and makes calculating the mean impossible, since we do not know exact values for this field).
   4. You have data measured on an ordinal scale.
3. Mode is the preferred measure when data are measured in a nominal ( and even sometimes ordinal) scale.