ABSTRACT
Measures of central tendency, such as the mean and the median described in Chapter 2, provide useful information. But it is important to recognize that these measures are limited and, by themselves, do not provide a great deal of information. There is an old saying that provides a caution about the mean: “If your head is in the freezer and your feet are in the oven, on average you’re comfortable.” To illustrate, consider this example: Suppose I gave a sample of 100 fifth-grade children a survey to assess their level of depression. Suppose further that this sample had a mean of 10.0 on my depression survey and a median of 10.0 as well. All we know from this information is that the mean and the median are in the same place in my distribution, and this place is 10.0. Now consider what we do not know. We do not know if this is a high score or a low score. We do not know if all of the students in my sample have about the same level of depression or if they differ from each other. We do not know the highest depression score in our distribution or the lowest score. Simply put, we do not yet know anything about the dispersion (i.e., the spread) of scores in the distribution. In other words, we do not yet know anything about the variety of the scores in the distribution.
