Discussion Overview

In society, we often jump to conclusions about data by saying that one variable causes another variable to change. For example, if a black cat walks in front of you, and then you lose $20, you may assume that the black cat gave you bad luck and caused you to lose the $20. Although these two actions happened one right after the other, this does not imply that one caused the other to happen. Whereas, if you are unable to eat lunch and then become hungry, these two actions have a strong correlation and causation because one causes the other to occur. This misconception between correlation and causation is an important topic to understand to process information logically.

In the post, describe the difference between correlation and causation. Provide one example of a pair of variables that have a strong correlation but lack causation. Provide a second example of a pair of variables that have a strong correlation and a strong causation. Explain the difference between these examples. Why might assuming causation be a problem?