# Real Life application of Bayes theorem

## General info about Bayes Theorem

Before examining how to apply it to everyday life, it's obviously important to understand the theory behind it and get an intuitive understanding of how it works:

- The Wikipedia article about Bayes Theorem looks good.

- Then there's the classic introduction to it by Eliezer Yudkowsky: An Intuitive Explanation of Bayes' Theorem

- There's also an awesome playlist that explains Bayes theorem in a intuitive and accessible way: Bayes' Theorem for Everyone

## Bayes Theorem Skill page

## Examples

Bayes theorem is frequently recommended by rationalists as a way to make better predictions, have accurate beliefs, and thus take better decisions. But is it really useful in everyday life? This section is intended for examples of application of Bayes that could lead to better decisions in problems that are likely to be faced by a large part of the population (as opposed to problems specific to a certain profession or generally unrealistic situation.)

### Real life examples

For problems that were actually faced by someone. Both positive and negative experiences expected (in the sense of optimal solution to the problem).

Expected speculation about why a particular problem was well or ill suited for bayesian analysis (eg. availability of accurate priors).

- Where bayes was applied

How did it turn out? Why did you choose to apply bayes? Was that a wise course of action (applying bayes)?

- Where bayes wasn't applied

How did it turn out? Why did you choose not to apply bayes? Was that a wise course of action (not applying bayes)?

### Fictional examples

For problems that could hypothetically happen (both those situations where it would be useful to apply bayes and those were it wouldn't, and reasons for that).

- Problems where applying bayes is advisable

- Problems where don't even try it

Highly relevant: When (Not) To Use Probabilities

### Possible experiment

We make a group make decisions (in real life, researching actual probabilities, or maybe just in some kind of game context?)

based on information derived from bayesian inference, another group makes decisions intuitively. Everyone logs thought process

and reports consequences (good vs bad outcome).