Managers and professionals often pay more attention to the levels of their measures (means, sums, etc.) than to the variation in the data (the probability patterns/distributions that describe the data).  For the measures you identified in Discussion 1, why must dispersion be considered to truly understand what the data is telling us about what we measure/track?  How can we make decisions about outcomes and results if we do not understand the consistency (variation) of the data?  Does looking at the variation in the data give us a different understanding of results?

Answer Preview-Making Use of Data Dispersion and Variation Levels