While banning ice cream trucks from entering your neighborhood may sound far-fetched, when it comes to problem solving, the above paragraph describes a common issue of misunderstanding the difference between correlation and causation. This misunderstanding can influence our decisions, sometimes with serious consequences that ripple throughout a community.
When two things are related, but one does not cause the other then it is correlation, not causation. Usually this means the two are in some way related to a third factor, but not always. If you have a big enough pile of data, you will even find relationships that are purely coincidence, like the strong relationship between the sale of margarine and divorce in the state of Maine.
Unlike correlation, to claim one thing actually causes another thing to happen means you need to be able to demonstrate an actual cause and effect relationship, preferably a strong relationship. Arguably the gold standard of cause and effect is physics, but for an example I will use the pharmaceutical industry.
To make the claim that a particular drug causes a certain effect, such as lowering your cholesterol or growing hair, the FDA requires that pharmaceutical companies support those claims, putting the drug through a rigorous, four phase, twelve step process that takes roughly 12 years. The process is strictly regulated using control groups and clinical trials to test the drug, making sure that X causes Y and that the drug is safe (relatively speaking). The acceptable error rate can go as high as 5% for some drugs. This means that the clinical trials prove that there is a 95% chance the drug does actually cause hair to grow. Other drugs are held to an even stricter standard, requiring proof up to 99% effectiveness.
As you can see, causation is difficult to prove, especially the more variables that are involved. No wonder it takes 12 years just to prove a pill causes hair to grow.
The Bottom Line
Personally, I do suspect that researchers are onto something with this whole fructose thing, but this article is not really about discussing the relationship between ice cream and forest fires or obesity. Instead, I want to reinforce the major difference between correlation and causation. When it comes to your ability to be a better problem solver, understanding this difference is critical.
In the real world, away from laboratories and clinical trials, on the news, in boardrooms and coffee shops, everywhere you go, you will hear claims that X causes Y. From politics to the weather, from the stock market to personal relationships it is human nature to try and explain things, to create stories that make sense. As you hear these stories or as you create the story, try to keep in mind one thing, that correlation is not causation.
Huff, D. (1993). How to Lie with Statistics (Reissue ed.). W. W. Norton & Company.
Silver, N. (2015). The Signal and the Noise: Why So Many Predictions Fail--but Some Don’t. Penguin Books.
Stanovich, K. E. (2012). How to Think Straight About Psychology (10th Edition) (10 ed.). Pearson.
Vigen, T. (2015). Spurious Correlations. Hachette Books.
About the author:
Richard Feenstra is an educational psychologist with a focus on innovation, problem solving and productivity. His work experience includes military service, law enforcement, fire prevention and workplace safety. Richard is also a recognized expert witness regarding issues of safety and security. Richard holds an M.S. in workforce development and a Ph.D. in learning and technology.