If you have a disease test which is 99% accurate, and it’s diagnosed a patient positive, what is the probability that a patient indeed has the disease? 99%? Wrong! Think again! You really can’t tell unless you know the probability of the disease itself, i.e. how likely a random patient is to have the disease. For a quite uncommon one, this single test may present confidence of 1% or even less. Although obvious to a statistics specialist, common people are usually totally stumbled by it, and find it very hard to grasp. I won’t explain here why it is so, since I doubt I’ll do a better job than the speaker I’m about to present.

Peter Donnelly is an Oxford mathematician, specializing in applied probability. In this highly educational (and quite jaw-dropping for most) talk, he reveals common mistakes in interpreting statistics – and the devastating impact they can have. This is a great presentation of how important statistics is, and how crucial, and yet so uncommon, it is to understand it.

There are so many ways to misuse or misinterpret statistics, with one of the favorites being, of course – correlation implying causation. And yet, I believe statistics is a cornerstone of modern science. It may not play such a central role in theoretical physics, of course, but medicine, sociology, cognitive science, etc. etc. all depend on it to interpret the results of experiments correctly. If we all had better education in this area, we maybe wouldn’t be interviewing successful people so much.

Please watch this talk. It might be a little boring in the beginning, but it gets much more fun pretty soon.

Original video on TED.com (might be better quality).