# Lesson Idea: Explaining the difference between causation and correlation

Travis DixonResearch Methodology

Does the browser you use affect how good you are at your job?

This lesson works well with in the introductory unit, topic 1.2, lesson (d) “correlation.”

The following TED Talk by Adam Grant is really interesting for a number of reasons and it’s well worth a watch. I like to show students the short segment from 8:25 to 10:30 where he talks about how mozilla firefox and google chrome browser users outperform their safari and internet explorer colleagues, and they also stay in their jobs 15% longer. I stop the video just before he gives a way the answer.

I like to deliberately mislead students in interpreting this study as it makes for a nice bridge from discussing controlled experiments to quasi-experiments and the difference between causation and correlation.

I first ask them to work with a partner to identify the IV and DV and remembering what they’ve learned from previous lessons about drawing conclusions from studies, what do they think the conclusion is from this study?

Just to note: the IV in the study is the type of browser used and the DV is the job performance.

It usually works that at least a couple of students will conclude something like, “the type of internet browser you use affects how good you are at your job.” Or perhaps they might say something like, “if you want to get better at your job you should use google chrome or mozilla firefox.”

After we discuss their conclusions we continue watching the video where Grant explains the results. These are clearly correlational, by the way, as his explanation is based on the idea that safari and explorer come pre-installed. To use mozilla or chrome you need to be the sort of person who explores other options and you’re willing to try something new to do something better. So it’s the character of the individual that is choosing the browser, which is influencing job performance, rather than the browser itself.

I use this basic activity as a hook and a chance to explain the difference between causation and correlation between variables, and how important it is to think critically about conclusions from studies. The obvious conclusion to draw is usually causational, but we must learn to move beyond drawing the obvious conclusion to think carefully if there are alternative explanations. We then move into exploring more examples of correlational studies in an activity that I’ll post about soon.