Conditional probability (Bayes' Theorem) explained visually

Visualize why evidence alters our confidence (probability) of prior events leading to Bayes theorem. This formula is explained using a tree analogy. This video acts as an introduction to Bayesian statistics.

Thanks a bunch to Kalid Azad for reviewing this lesson.

[sophiesheu]

Why isn’t this video getting millions of views? I watched four other videos on Bayes' Theorem before finding this one, and it's hands-down the most intuitive and straightforward explanation for calculating posterior probability. If the video could emphasize that Bayes' Theorem is about updating our belief about an event (e.g., believing a fair coin is flipped) based on new evidence (e.g., getting 'heads'), it would be even better.

[BVB_rose]

Even after 9 years, this might be the most helpful video that explains his theorem. Thanks!!

[aball]

Fucking awesome job explaining it with a tree diagram. Don't even need to know the formula now

[calebredman]

This is a useful video. It is short and engaging so the average person will probably watch the whole thing and in my opinion does a decent job at explaining Bayes’ theorem. If you are going to redo or further flesh out this video like you said in an earlier comment I would like to make a few suggestions. I think you should introduce the equation at the beginning of the video and show how the parts correspond to the results of the coin flip. This way if someone has to work more directly with the equation they can still relate it to the example and the tree.

[diego]

please please please know that there are people that appreciate everything you do, and how you do it! You could make an entire series on bayes theorem! (which I recommend!)

[anwang]

I've been looking for a good way to explain Bayes theorem for ages. Excellent job, keep up the good work!

[oliverb]

YESS Thank you so much! Both your videos on bayes theorem and entropy are great. This is what students like me need, not just some equation you're told to just accept.

[ssjiv]

Thank you, Bob.
I know my Bayes Theorem now :P

Excellent explanation. :)

[saltcheese]

BRILLIANT!
was going around youtube looking for some intuition about the concept; many videos, nothing to really implant the ideas in my head. but than i went to khanacademy.

[jydk]

Love the ending! If you look at the logic of this idea in reverse you should see how it could be used to an advantage just by virtue of understanding it.

[nisserot]

Thank you for this video. It finally clicked in my mind after trying to understand this concept from multiple sources. Understanding this concept may make the difference between passing or failing my stats and probabilty exam :) Again, thank you. Great presentation style.

[alex]

U people are making a topic i assumed to be boring really enjoyable, keep up guys good work

[plekkchand]

Good video, though it would help to explain a bit further why there must be the same number of branches coming from each of the three possible coin choices in the third example here.

[saniulhaque]

The best way to make someone understood, thanks sir

[syphyt]

I've been watching your videos from the very beginning, and I've never stopped loving them. (: From a fan, thank you.

[antney]

Good, fun, informing video . Highly recommend it.

[oblivitus.]

This really helped me grasp it, thank you!

[JJJ_AA]

So you are the guy from my maths text who is doing all the experiments of tossing coins😂 lol. I appreciate the hard work that you have put in ❤

[midevil]

Kept my attention the entire way through, nice!

[petechapman]

Delightful. My only criticism is that you might be tough on viewers that aren't Canadians. On a Canadian quarter both sides are "heads" (Liz II and a caribou). I realize that one of them is an animal but it's still a head. In a sense Canadians flipping quarters are never playing with a fair coin. Still, it's a great description of Bayes' Theorem and as a Canadian you've made me very proud.