The medical test paradox: Can redesigning Bayes rule help?


From 3Blue1Brown.

Bayes factors, aka Likelihood Ratios*, offer a very clear view of how medical test probabilities work.
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The book by my friend Matt Cook about paradoxes mentioned at then end:

On the topic, I can’t help also mentioning another paradox book I’m rather fond of by Bunch:

*As mentioned in the on-screen note at the end, while the terms "Bayes Factor" and "Likelihood Ratio" refer to the same term 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:

This post has some nice discussion of the distinction:


These animations are largely made using manim, a scrappy open source python library:

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.
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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:

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