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#bayestheorem#probability#likelihoodratio

The medical test paradox, and redesigning Bayes' rule

3Blue1Brown

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  • A classic. People kept recommending it from 2020 to 2025.
  • Presented by Grant Sanderson, creator of 3Blue1Brown
  • Linked in the description3b1b/manim

Beginner. No background in the topic needed.

1.4M views5 years ago5 recommendations

Computer scienceAI and machine learningMath for programmersClassic machine learning

Grant Sanderson explains the medical test paradox and reframes Bayes' rule using likelihood ratios to make probabilistic reasoning more intuitive.

About Likelihood Ratios, also sometimes called Bayes Factors*. Help fund future projects: patreon.com/3blue1brown An equally valuable form of support is to simply share some of the videos. Special thanks to these supporters: 3b1b.co/bayes-factor-thanks Home page: 3blue1brown.com The book by my friend Matt Cook about paradoxes mentioned at the end: amzn.to/3aBrEzg On the topic, I can't help also mentioning another paradox book I'm rather fond of by Bunch: amzn.to/3mBDSKE Another video on Bayes' theorem: youtu.be/HZGCoVF3YvM *As mentioned in the on-screen note at the end, while the terms "Bayes Factor" and "Likelihood Ratio" refer to the same ratio in this setting, where Bayes rule is used on the probability of an event with only two possible outcomes (you either have the disease or you don't), they do take on divergent meanings in more general contexts. Namely, if you have a continuous parameter you are trying to estimate, the two terms reflect two alternate approaches you can use in comparing hypotheses. In fact, some people take the phrase "Bayes factor" to _specifically_ refer to its use in this more continuous context. If you want more details, Wikipedia actually has a really nice example discussing the difference: en.wikipedia.org/wiki/Bayes_factor#Example This post has some nice discussion of the distinction: stats.stackexchange.com/questions/27345/likelihood-ratio-vs-… Timetable: - What is the paradox? - The Bayes factor - The snazzy Bayes rule - Contrast with the usual formula Thanks to these viewers for their contributions to translations French: Raphaël Ventura Russian: Ilia Konnov Vietnamese: duyhub ------------------ These animations are largely made using manim, a scrappy open source python library: github.com/3b1b/manim If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and it has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by Vincent Rubinetti. Download the music on Bandcamp: vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown Stream the music on Spotify: open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people. ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe: 3b1b.co/subscribe Various social media stuffs: Website: 3blue1brown.com Twitter: twitter.com/3blue1brown Reddit: reddi

Why it's here

5 people recommend this

What people say. Highly recommended as a brilliant and intuitive introduction to Bayesian probability and updating odds.

drewmateon Hacker News • Recommends

On “1937 film explains how a car differential works”

…e absolutely phenomenal - the best educational content I've seen on YouTube. I cannot recommend them highly enough. In my experience it is more akin to reading a (very engaging) textbook than watching a video. I often find myself pausing to think things through and can _feel_ myself grasping at new ideas and learning while watching. 0 link 1 link to this video…

inactive-useron Lobsters • Recommends

The medical test paradox: Can redesigning Bayes rule help?

kilotarason Hacker News • Recommends

On “U.S. to require negative Covid-19 tests for international air passengers”

Almost no tests in existence can confirm or deny particular infection. Think of them (and symptoms) more as odds updater. If you have 20 minutes to spare this video 0 by 3 Blue 1 Brown is a brilliant introduction to the concept. 0 link to this video

andersrson Hacker News • Recommends

On “The cancer that some doctors don't want to call cancer”

3Blue1Brown explains it well. link to this video If the population who do _not_ actually have serious cancer is orders of magnitudes larger than those who do then there will be far more people in the false positive group than the true positive group and thus the vast majority of operations will be unnecessary.

V__on Hacker News • Recommends

On “Everything is correlated (2014–23)”

I really like this video 1 from 3blue1brown, where he proposes to think about significance as a way to update the probability. One positive test (or in this analog a study) updates the probability by X % and thus you nearly always need more tests (or studies) for a 'meaningful' judgment. 1 link to this video

lovecgon Hacker News • Mentions

On “The Prosecutor's Fallacy (2018)”

For me, taking the logarithm makes it even more intuitive somehow - the test becomes something like a modifier in an RPG-style game. In your example, the test gives +2 to the possibility of having a disease, a more powerful test could be +3, etc. Adding all the different +/- modifiers works just as well, and all this math is easy to do in one’s head. Edit: also, obligatory 3blue1brown video link to this video

mananaysiempreon Hacker News • Mentions

On “The Prosecutor's Fallacy (2018)”

The advantage of the odds formulation is that you can compute posterior odds in your head, whereas computing the posterior probabilities via the standard form of the theorem (that you wrote down here) involves an unpleasant normalization factor. E.g. for the example in link : prior odds of infection = 2 : 98 × likelihood ratio = 95 : 5 = posterior odds ≈ 200 : 500 and thus the probabilities (if you actually need them at this point) are ≈ (2/7, 5/7). See also 3Blue1Brown’s exposition: link to this video .

conartist6on Hacker News • Mentions

On “Major AI conference flooded with peer reviews written by AI”

Usually the detectors are only called in once a basic "smell test" has failed. Those tests are imperfect, yes, but Bayesian probability tells us how to work out the rest. I have 0 trouble believing that the prior probability of an unscrupulous individual offloading an unpleasant and perceived-as-just-ceremonial duty to the "thinking machine" is around 20%. See: link to this video