Although a measure of central tendency is certainly important, it does not completely represent a distribution by itself. Given a measure of central tendency, you have an idea of where scores tend to fall, but you don’t know to what extent the scores differ from one another. Consider the members of your psychology class. They would vary on a host of measures, including height, weight, and grade point average. A measure of the amount of dispersion contained within a data set is called a measure of variability. Except when all scores in a data set are identical, all sets of scores vary to some degree. Whereas the measure of central tendency gives you the best representation for an entire set of data using a single value, the measure of variability tells you just how good of a representation that value is. Just because the measure of central tendency is the best representation does not ensure it is a good one, just as my best attempt to bake a cake does not ensure that the cake will be good. Measures of variability include the range, the variance, and the standard deviation.