This is the true test of compensation geekdom. The real geeks just sat forward in their chairs in eager anticipation of a meaty statistics debate. Everyone else either felt their eyes begin to glaze over, or ran for the hills.
A recent exchange with fellow Minnesota blogger Lisa Rosendahl (if you aren't reading Lisa's blog, start today) reminded me that it's been a few years since I tackled this topic, so here it comes once again.
Pay surveys present us with a variety of descriptic statistics to choose from in our efforts to review or develop new compensation structures and practices. Since most of us are seeking information on the middle of the market, we are typically presented with a choice between the following two measures of central tendency:
Mean - the mathematical average, calculated by adding up all the pay rates in the data set and then dividing by the number of pay rates in the data set.
Median - the value of the pay rate that falls in the middle of all the rates in an ordered data set (that is to say, a data set which is ordered from lowest to highest rate).
(The other measure of central tendency, which rarely if ever shows up in compensation surveys, is the mode, or the pay rate which occurs most often in the data set.)
I have a strong preference for the median over the mean. And I found a great explanation of my preference in an old Psychology textbook (Psychology, the 8th Edition, by David G. Myers, Worth Publishers):
With income distribution, the mode, median, and mean often tell very different stories. This happens because the mean is biased by a few extreme scores. When Microsoft Chairman Bill Gates sits down in an intimate cafe, its average (mean) patron instantly becomes a billionaire.
In other words, the mean pay rate in a pay survey will be affected by any extreme pay rates in the data set, where the median will not. For this reason, I believe the median (where it is offered; some surveys only provide the mean) is a better and more reliable measure to use in pay program assessment and design.
Hit me with any dissenting opinions in the comments!
Image: Creative Commons Photo "Free mixed numbers" by D. Sharon Pruitt