When graphs and statistics lie

I should be clear: statistics don't lie, people lie. One of the easiest ways to trick your audience is to confuse correlation with causation.

Correlation versus causation

I don't think I would be allowed to publish this book without taking a deeper dive into the differences between correlation and causation. For this example, I will continue to use my data for TV consumption and work performance.

Correlation is a quantitative metric between -1 and 1 that measures how two variables move with each other. If two variables have a correlation close to -1, it means that as one variable increases, the other decreases, and if two variables have a correlation close to +1, it means that those variables move together in the same direction; as one increases, so does the other, and the same when decreasing.

Causation is the idea that one variable affects another. For example, we can look at two variables: the average hours of TV watched in a day and a 0-100 scale...