Mental Models - Bayes' Theorem ✨
Fool me once, shame on you; fool me twice, shame on me.
Bayes’ Theorem comes to us from probability, and is crucial if you wish to think clearly, and not be fooled twice.
What is Bayes' Theorem?
Bayes' Theorem helps answer the question “What is the chance that…”.
It is the formal method to determine the probability of an event based on the occurrences of prior events. It’s used so that we can revise prior probabilistic beliefs – and change our minds – in response to new evidence.
You start with an initial probability, then consider new evidence, and then revise the initially assigned probability based on the new evidence.
Why is this interesting?
Bayes’ Theorem can be done formally with math and your dataset, or you can adopt a Bayesian mindset and use it informally as well.
Insanity is doing the same thing over and over again and expecting different results.
The key to being a Bayesian thinker is remembering that everything isn’t binary, but that it has a probability. In other words things are not black and white, but shades of grey.
Bayesian thinkers update their probability assessment when they encounter new info, as Einstein was alluding to.
Want to go deeper?
👨🌾 I think the English are talking about being a Bayesian thinker when they say
Don’t keep stepping on the same rake
🔖 Here’s a good longer write up on Bayes Theorem, to understand the underlying equation
🤔 In a good Bayesian updating opportunity, it turns out that Einstein didn’t actually say that “insanity is doing the same thing over and over again and expecting different results”, it’s commonly misattributed.
🤯And to truly blow up the Bayesian updating opportunity - and undermine the whole post - in quantum mechanics you can do the same thing many times and get different results.
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