Thus far we have seen the related statistical sins of cherry picking and data dredging. Today, let’s talk about the false cause fallacy (or “false causality” for short), which occurs when you observe two events that appear together and then leap to the conclusion that one event must have caused the other. (Here is a mundane example. The video below presents many more.) In reality, just because two events occur together does not mean that one caused the other. The causation may run in the opposite direction or some unobserved third factor might be the underlying cause of both events or there might be no direct or indirect causation at all!