Levels. Managers and
professionals often pay more attention to the levels of their measures (means,
sums, etc.) than to the variation in the data (the dispersion or 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?