Correlation vs Causation

This week we've looked at a lot of data and calculated the correlation coefficients. Remember, correlation means two variables are related to each other in some way. Sometimes the correlation coefficient indicates that the relationship between the two variables is strong, sometimes it's weak and sometimes it's nonexistent.

At this point of your learning, it's worth reminding you that:

CORRELATION IS NOT CAUSATION!

It's easy to think that if two variables have a high correlation coefficient that one must cause the other, but that's not always the case.

Just because two variables have a strong correlation coefficient does not mean that one variable causes another to happen.

Let's say we have two variables, A and B. They share a very strong correlation coefficient. Here are the possibilities for causation:

  • A and B are correlated
  • A causes B
  • B causes A
  • A and B are both caused by some other variable X
  • Both A and B cause each other (in a loop)

Watch the video posted below to learn more about this topic and to see why strong correlation in a scatter plot does not always indicate causation:

Last modified: Saturday, 2 December 2017, 5:00 PM