The Deadly Mistake: Confusing Correlation with Causation
Emily Rauscher humorously quipped, “Everyone who confuses correlation with causation eventually ends up dead.” While this statement might seem absurd at first glance, it cleverly illustrates a crucial point about the nature of correlation and causation.
Understanding the Difference
Correlation refers to a statistical relationship between two variables. When two things are correlated, they tend to occur or change together. Causation, on the other hand, implies that one thing directly causes the other to happen.
The problem arises when we mistakenly assume that because two things are correlated, one must cause the other. This logical fallacy is so common and potentially harmful that Stephen Jay Gould, a renowned evolutionary biologist, stated:
“The invalid assumption that correlation implies cause is probably among the two or three most serious and common errors of human reasoning”
Real-World Implications
This error in reasoning can lead to misguided decisions in various fields, from public policy to personal health choices. For instance, a study might show a correlation between coffee consumption and longevity. However, assuming that drinking coffee directly causes longer life would be premature without further investigation into other factors like lifestyle, diet, or genetics.
When I was a kid there were some “bad kids” that were causing trouble at school. These kids listened to heavy metal music and some parents jumped to the conclusion that the music caused their bad behavior. This is a correlation but probably not the cause. It is more likely that these troubled kids were attracted to that type of music.
The Importance of Critical Thinking
To avoid falling into this trap, it’s crucial to approach correlations with a critical eye. Ask questions like: Could there be a third factor influencing both variables? Could the relationship be reversed? Is there a logical mechanism that could explain a causal relationship?
It is often the case that a third factor is actually causing both correlated variables to move together.
Without testing these observations and determining an actual cause we don’t know.
It goes back to Emily Rauscher’s quote – “Everyone who confuses correlation with causation eventually ends up dead”
While Rauscher’s quote playfully reminds us that correlation doesn’t imply causation (after all, everyone dies eventually, regardless of their reasoning skills), Gould’s statement underscores the serious nature of this common error. By understanding the difference between correlation and causation, we can make more informed decisions and avoid potentially harmful misconceptions.
