One of the biggest challenges in medicine and science is understanding correlation and causation.
Medicine has become increasingly evidence-based. But what does the evidence really say? Is a study signal or noise? Does enough correlation mean causation (e.g. smoking causes cancer)? How much hope can we put on big data?
In the last few years, I’ve been on a quest to understand evidence. I am in the beginning stages–akin to a cat-4 bike racer.
Sometimes a piece comes along that really helps.
This one is long. You can’t read it while waiting in line for coffee. It takes time to digest it all.
But I think it helps explain the many biases of evidence.
It’s called Correlation, Causation and Confusion.
Dr. Anish Koka (@anish_koka) https://twitter.com/anish_koka
Dr. Saurabh Jha (@roguerad) https://twitter.com/RogueRad