Correlation vs. causality

Recently, Stat-Ease Founding Principal, Pat Whitcomb, was interviewed to get his thoughts on design of experiments (DOE) and industrial analytics. It was very interesting, especially to this relative newbie to DOE. One passage really jumped out at me:

“Industrial analytics is all about getting meaning from data. Data is speaking and analytics is the listening device, but you need a hearing aid to distinguish correlation from causality. According to Pat Whitcomb, design of experiments (DOE) is exactly that.

‘Even though you have tons of data, you still have unanswered questions. You need to find the drivers, and then use them to advance the process in the desired direction. You need to be able to see what is truly important and what is not,’ says Pat Whitcomb, Stat-Ease founder and DOE expert. ‘Correlations between data may lead you to assume something and lead you on a wrong path. Design of experiments is about testing if a controlled change of input makes a difference in output. The method allows you to ask questions of your process and get a scientific answer. Having established a specific causality, you have a perfect point to use data, modelling and analytics to improve, secure and optimize the process.’"

It was the line ‘distinguish correlation from causality’ that got me thinking. It’s a powerful difference, one that most people don’t understand.

As I was mulling over this topic, I got into my car to drive home and played one of the podcasts I listen to regularly. It happened to be an interview with psychologist Dr. Fjola Helgadottir and her research into social media and mental health. As you may know, there has been a lot of attention paid to depression and social media use. When she brought up the concept of correlation and causality it naturally caught my attention. [And no, let’s not get into Jung’s concept of Synchronicity and whether this was a meaningful coincidence or not.]

The interesting thing that Dr. Helgadottir brought up was the correlation between social media and depression. That correlation is misunderstood by the general population as causality. She went on to say that recent research has not shown any causality between the two but has shown that people who are depressed are more likely to use social media more than other people. So there is a correlation between social media and depression, but one does not cause the other.

So, back to Pat’s comments. The data is speaking. We all need a listening device to tell us what it’s saying. For those of you in the world of industrial experimentation, experimental design can be that device that differentiates the correlations from the causality.


For the full article on Pat Whitcomb, visit