Often, simply understanding totals, sums, and even the breakdown of the part to whole only gives 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 such as:

• 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 the average score were most students' test scores?

These questions all have similarities. In each case, you seek an understanding of where individuals (patients, components, students) were in relation to the group. In each case, you most likely have a relatively high number of individuals. In data terms, you have a dimension (patient, components, and student) with high cardinality (a high number of distinct individual values) and some measure (length of stay, life expectancy, and test score) you'd like to compare. Using one or more of the following visualizations might be a good way to do this.