Communicating Data with Tableau

Chapter 8. Multiple Quantities

“Correlation does not imply causation.”

—Unknown

So far, we’ve been creating views that focus on one variable at a time. While focus can be great, there is a whole world of relationships between multiple variables to explore, understand, and communicate. Finding these relationships can change the world (think carbon dioxide and global temperature).

One thing to keep in mind when exploring two or more variables at a time is that “correlation does not imply causation.” What does this oft-quoted phrase mean? Just because two variables seem to change together doesn’t necessarily mean that one causes the other to change, or vice versa. A third factor could be causing them both to change, or it may be coincidence and there may not be any causal relationship at all.

You’ve probably heard the example of rising ice cream sales and shark attacks. They both may rise together, but as the college textbook example goes, they’re both caused by increasing numbers of people at the beach, which is in turn caused by increasing temperature. It’s a silly example intended to illustrate the point, but there’s some truth to it. We’re very quick to assume causal relationships exist when all we really have is evidence of correlation. It’s something to watch out for, but we shouldn’t let it derail our exploration altogether, either.

In this chapter, we’ll consider a number of ways to explore and communicate multiple quantities in the same individual ...