Conditional Probabilities, Clearly Explained!!!
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- เผยแพร่เมื่อ 18 มิ.ย. 2024
- One the most fundamental concepts in Probability, Statistics and Bayesian Statistics is Conditional Probability. In this StatQuest, we walk you through what Conditional Probabilities are and how to calculate them using raw data as well as unconditional probabilities. BAM!!!
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0:00 Awesome song and introduction
1:11 Filling in a contingency table
4:45 Calculating conditional probabilities from counts
8:00 Calculating conditional probabilities from unconditional probabilities.
#StatQuest #Probability #Bayesian
NOTE: Conditional probabilities can be written two ways: a "long" version and a "short" version. In this video I use the long version (because it's easier for me to read)...
P(A and B | B) = P(A and B)/P(B)
...however, the short version is commonly used...
P(A | B) = P(A and B)/P(B)
Both mean the same thing, in other words: P(A and B | B) = P(A | B). The short version implies the "and B" part.
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Your version is much easier for non-statisticians like me :)
@@exploringdiversity572 bam!
omg this cleared up so much confusion i had from reading my text book where they were using the shorthand
I love how I need a video that seems designed for 7 year olds to understand my uni course.
Great vid thanks
Glad it was helpful!
I like the style. It's fun and nostalgic
this is secondary school level maths
In India due to competition for engineering entrance exam teacher majorly focus on question types rather than concept in depth. Im glad there is a place where people learn for fun not just for competition, also appreiciate teachers like you who can turn subjects into magic. Keep up the good work! From now on I'm permanent on this channel. You are the best!
Thank you very much!
I always thought I was incapable of learning math, but it had always intrigued me. I just discovered what was missing; a good teacher. Thank you very much.
bam!
I'm in the same boat as you man, love this channel
Very nice video for sure. Conditional prob is one of the topics students feel the most difficulty when they start studying for stat! Great job!
Thank you! :)
Soda and Candy > Mechanical engineering degree. Thank you it was very helpful!
BAM! :)
I "stumbled" upon the contingency table method many years ago when trying teach myself the basics of probability. This approach is really the only approach for developing a solid understanding of conditional probability, as well as joint and marginal probability. I've never seen anyone present it this way and I'm so glad to see it here! Just more evidence that Josh Starmer is an absolute master instructor.
BAM! Thank you very much! :)
you know, me too.
Hey this table reminds me of confusion matrix from a ML classification problem
I should of paid you instead of my useless lecturer at university! This is so much more useful thank you!
Glad I could help!
A 6 years old can understand this. That's genius
Thanks!
this is how i wish people taught stuff, i really hope these ways of learning statistics get the recognition they deserve! please keep it up !!!!
Thank you!
Never thought that to understand conditional probabilities is this easy … thanks prof 🎯
bam! :)
Logical explanation, precision and genuineness - your way of teaching exemplifies these essential qualities. Thanks so much Josh for these great lessons. Invaluable effort!
Wow, thank you!
Thank you Josh & the team. I'm so grateful I've found your channel. You make me truly love statistics!
Glad you enjoy it!
This is one of the best explanations of absolutely anything I've ever seen and heard. Well done. Subscribed.
Awesome, thank you!
Today I discovered your channel and it is truly a gem. As a EEE student who tries to make sense of subject of probability and neural network stuff, I believe I will benefit a lot from your work and be thankful for rest of my life. Especially statistics is challenging as things get messy a lot and scary/unintuitive equations with confusing notations start to appear. I did not look at all of your videos yet, but I hope that you did statistics less scary for those like me. My best regards
Awesome! Here's a page that lists all of my videos (and playlists etc): statquest.org/video-index/
I love these classes. They are so easy to understand. Thks you for sharing with us!
Thank you very much! :)
THANK YOU! I've really been struggling to get an intuitive understanding of conditional probability and this has helped so much!
Glad it helped!
Great video!
I never saw the "long version" to write conditional probabilities before but it's better to understand. Very intuitive!
Yes, I like it more, too.
I love how the videos comprehend complex topics with easy-to-understand explanations. Thanks for all your effort :)
You're very welcome!
Awesome. I always come here to get my doubts clear. Love the way you teach. Lots of BAM!!!
Thanks!
Great job Josh!!!!! This is such a clear and precisely laid out video perfect for my students.
bam!
You are literally saving me from my statistics exam! Thank you so much, your explanations are brilliant and you are really making me focus with your simple and funny ways. Thank you again!
Good luck! :)
Awesome explanation. Thanks to you and Triple Bam members
Glad you liked it! BAM! :)
Struggled with this topic literally for years! You solved my problem within just 10 min.! 🎉
Bam! :)
Incredibly well-explained. Thank you!
Thank you! :)
Exceptional explanation... I'm Completely in love with your teaching style
Thank you!
You are a GOD of simple explanations
Thanks!
Thank you SO MUCH for clarifying that AB = A and B, when A and B are events. I get confused because XY is literally multiplication when X and Y are random variables. It can be doubly confusing because both events and random variables (even though they are different types) can be passed into P(), the probability function. Function overloading is a thing in computer programming, but I’m shocked to see it in math. Also, the implied A and B in P(A | B) is a lifesaver, many times over. BTW, understanding the tables in this video is pivotal in grasping marginal distributions, Baye’s Theorem, and joint probability distributions - it feels like I’m getting the other three concepts for free!
Triple bam!!! :)
Thank you so much! This is the best video on conditional probability on internet.
Thank you!
First, I'll admit I had "Do you love orange soda?" from Keenan and Kel in the back of my mind. That said, this video has been immensely helpful, thank you!
:)
You're incredible. You make everything seem so easy
Wow, thank you!
This is the best video explaining conditional probability. Hands down.
Thanks!
I get a dopamine rush every time this man says bam
bam!
Thank you! Great content always🤩
My pleasure!
Hi Josh, I am a long time fan of your channel and all of the shameless self-promotion has guilted me into buying one of your study guides (LDA). They seem great, a nice supplement to your videos. Though I think you would benefit greatly from stripping down the ordering process, I mean for online purchases almost noone asks for complete addresses, phone numbers and names anymore. I fought very hard with myself to finish it and parting with that information, and I succeeded simply because of being so grateful for the videos you provide.
You'd probably sell a lot more of these if you just ask for an email. Like most. Cheers
Thanks for the advice! I'll see what I can do to simplify the ordering process.
Best explanation ever. Thank you! ❤️
Thank you! :)
Thanks for this explanation.....It really help me to grasp this topic....
Bam!
Well explained. Bit long but makes the concept clear as possible. A big THANK YOU Josh.
Thanks!
This was amazing and fun. Thank you!
Glad you enjoyed it!
i love you this actually helped so much
Thanks!
"statsquatch" I love it 😂
Also I guess I never put together that you could use the probabilities to calculate conditional probabilies, super helpful
BAM! :)
And BAM! I subscribed….such a clear explanation
Awesome, thank you!
Super easy ti understand! Thanks alot
Glad it helped!
Happy Guru Purnima Sir, So blessed that this channel exists. ❤️🙏
Thank you! And thank you for supporting StatQuest! BAM! :)
❤️❤️
Thanks a lot. Clearly explained as you stated.
Thank you!
Conditional probability : Decoded. BAM !!! Tbh Some of your videos ( Very few actually. For example p-value 😁) were not very clear to me ( Well that's just me ! ) in the first instance but I digested through them somehow. But with little study & focus or just give a break for a while, the 2nd or 3rd look at your video makes the concept super clear to me and I don't need to look elsewhere. Thanks for making such videos and making statistics/probability easier.
I'm glad you are able to understand the concepts.
I was looking for sth like this recently, thanks for somehow read my mind XD (I guess some kinda probability?? BAM!
BAM! :)
Great video, perfect explanation, marvelous song, as always :D
I would love to see a video about Generalized Linear Models( other than logistic).
Thanks and I'll keep that topic in mind.
👍👍👍
The people demand a Montecarlo Simulations StatQuest!!!
That would be nice.
I'll keep that in mind. We may get to something like it when I cover bayesian statistics.
That could be nice
Very well explained as usual!! BTW, do you have any plans for making videos on stochastic calculus topics like Brownian motion, Ito integral, martingale measures etc. If so, I am looking forward to them.
I'll keep those topics in mind.
👍👍👍👍
That's a great one.thank you😊
Glad you liked it!
Bam!! , what a bam person to explain everything about statistics so bamly!!
Thanks!
Bamks!
amazing googleplex BAM! Man you did it again. BRAVO
Wow, thanks!
😭😭😭😭thanks a lot😭😭😭god bless you, u are doing a noble job, may you get all the riches, love and happiness in the world
Thank you very much! :)
Great job josh you are my favorite teacher !! , can you think about making a video about Multicollinearity? bam
I'll keep that in mind.
Excellent video..again ! So good to be hit by the triple bam !!! 😁
Hooray! :)
Hundred Bam!! Great explanation as always
Could you put the playlist link in the description, it will help us a lot, thanks.
Which playlist are you asking about?
You're the statistics buddha! The analogies and the calming voice!
Thanks!
This helped understand c prob. I am taking a mandatory into to stat class with no math experience and passion. Thanks
Good luck!
Incredible lecture!! Bamm😀
Thank you!
Finally, I understand this. BAM!!!!
Bam!
Hi Josh! I've been working with DEseq2 a lot. Is dispersion shrinkage on your TODO list for statquest? I realize it's a little more of a niche topic for bioinformatics than broadly applicable tho
I'll keep it in mind.
So bloody good!
Thanks!
BAMMM!!!!
Great work
Can you make video about bayesian classifier because it's depending on conditional probability
I've already done that. Here's the link: th-cam.com/video/O2L2Uv9pdDA/w-d-xo.html
Your way of teaching ! Bam!
Thanks!
This channel is the bible of " Making 'Understanding Statistics' Easy !"
bam! :)
SUPERB explanation... BAAAM !!!!!
Thanks!
Your channel is amazing, I wish I had discovered it earlier! Would you ever consider making the slides available for sale? I would absolutely buy them.
I have some of them for sale here: statquest.org/statquest-store/
Wow. Great explanation with nice song ❤️❤️
Thanks a lot 😊!
@@statquest welcome sir 🙏🏻
although that i calculate conditional probability during college, i cannot get the concept of it leaving a gap in my stat knowledge...
i stumbled in your video and i have a basic understanding about it. im just need a more examples regarding conditional probability to fully master this concept...
Thanks Josh for your videos and hoping for more topics about stats and data analysis.
I'm glad my video was helpful! :)
amazing! the best!!
Thank you! :)
I GET IT NOW THANK U SO MUCH
bam! :)
This is how every tutorial should be!
Thanks!
Thank you so much...Now I cleared. 😁🥰🥰
bam!
Great!
Which software have you used to make this video(especially animation)?
I give away all of my secrets in this video: th-cam.com/video/crLXJG-EAhk/w-d-xo.html
It's interesting notation that you keep the superfluous condition on the left side of the pipe. I.e. using p(c=0 and s=1 | s=1) instead of p(c=0 | s=1). Do you find students understand it better this way?
Yes. To me it is much clearer.
Awesome! BAM!!!
:)
Good one Josh. Any plans of making videos on "Time series Models" ?
One day! :)
Thank you so much for this video! It saves my life hahaha
Thanks!
thank you, uncle bam!
My pleasure!
amazing!!!!
Thanks!
After 4:40, I cried "that's enough" because it was relentless!!
bam
Hi ! I really like yours graphics and pictures ! What tool do you use ?
Keynote.
@@statquest thank you :)
"I hate using abbreviations"
And I love you for that
bam!
Josh , Please start a series for mathematics concepts for machine learning. Such as probability, linear algebra, statistics, calculus etc.. It'll great help for us.
I have a bunch of statistics videos here: statquest.org/video-index/
best video ever !
:)
Because of you, I knew this Concept "BAM" 😹😸
bam!
Bam! I like the way you teach.....keep up!....Bam Bam!😅
Thank you very much! BAM! :)
Beautiful ❤
Thank you! :)
good job
Thanks!
Any chance you could post a video on when to run regular linear regression vs. time series ARIMA?
I'll keep that in mind.
Quadra BAMMMM ! ITS now clearly cleared in my mind
It's not clear? What time point, minutes and seconds, was confusing?
@@statquest sorry sir , I wish to write now but due to my mistake it become not .
Excellent excellent excellent
Many many thanks!
Good video that cleared up a lot of my questions. Only thing I'm confused now tho is 9:20 let's say we are given all the probabilities. How do I know if a probability is p(not love c and loves s | loves s) or p(loves s | not love c and loves s)? Confused because we know both the numerator and denominator. Thanks
Well, the second one, p(loves s | not love c and loves s), really doesn't make sense to calculate at all. This is because the conditional part, "not love c and loves s" is only a subset of "loves s", and thus, the conditional part does not give us any useful information about the larger "loves s" population.
@@statquest Thank you!
So cool!
bam!
@@statquest💥
does it applies to all questions of conditional probability or any conditions?
I'm pretty sure it does.
love it
Thanks!
Would love to see a video about KL divergence.
Me too and I hope to cover it sometime soon.
Hi Josh , how do we proceed if we have 3 variables ?(Soda Candy and Wafers) ?
The same way, we just end up with a more complicated table.
This might sound like a silly question but, shouldn’t one expect that P(A | ~A) = 0, and vice-versa? However this conditional notation doesn’t encompass that
Actually, it does. What you are asking is "The probability that someone loves candy AND does not love candy given that we know that they love candy". This results in the equation p(loves candy & does not love candy) / p(loves candy) = 0 / (6/14) = 0.
@@statquest thanks a lot prof for your clarifications!