Visualizing distributions
Often, simply understanding totals, sums, and even the breakdown of part-to-whole only gives you a piece of the overall picture. Many times, you'll want to understand where individual items fall within a distribution of all similar items.
You might find yourself asking questions like these:
How long do most of our patients stay in the hospital? Which patients fall outside the normal range?
What's the average life expectancy for components in a machine and which components fall above or below that average? Are there any components with extremely long or extremely short lives?
How far above or below "passing" were most students' test scores?
These questions all have similarities. In each case, you are asking for an understanding of how individuals (patients, components, and students) compared with each other. In each case, you most likely have a relatively high number of individuals. In data terms, you have a dimension (Patient, Component, and Student) with high cardinality...
