Hi again! Today, I am writing about Bayesian traps which I have recently re-discovered in a video by Veritasium. This is part 2 of a 3 part post about mathematic and statistical oddities. If you haven’t seen the previous post about confirmation bias check it out here! OK, so let’s get into it. The example I remember about goes more or less like this:

*You hear about this terrible disease going around that affects around **1:10000** people. This disease is lethal and asymptomatic so there is no easy way for you to know if you have it. *

*You decide to go to the doctor and she tells you there is a simple blood test you can take and the test is 9*

*9% accurate**.*

* You decide to take the test and a week later the results come in. The test came out positive!*

The question now is, should you start writing your will?

**No**you have nothing to worry about (although some would argue that it is probably a good idea to write a will in any situation). The clue lies in the fact that as humans we have a hard time dealing with large numbers. Here is what 10.000 boxes (imagine they are people) look like: One of those people will be marked in Red. That is the person that actually has the disease. If you were to test all these people using the

**99% accurate**test then

**1%**of all those tests would have come out as

**False Positives**(blue boxes): Which means that the probability of you

**having the disease knowing that you tested positive**is the same as the probability of you being that red box on the space of all the blue and red boxes put together: In a nutshell, you have about

**1% chance of having the disease!**

I hope you liked this example, next time I will write about the Simpson’s Paradox which is probably the scariest of them all. See you there!

so good. Loved it