NOTE: A lot of people ask "What happens when the original collection of measurements is not representative of the underlying distribution?" It's important to remember that a confidence interval is not guaranteed to overlap the true, population mean. A 95% CI means that if we make a ton of CIs using the same method, 95% of them will overlap the true mean. This tells us that 5% of the time we'll be off. So yes, a sample that is totally bonkers is possible, but rare. Understanding this risk of making the wrong decision, and managing it, is what statistics is all about. Also, at 5:55 I say there are up to 8^8 combinations of observed values and possible means, but this assumes that order matters, and it doesn't. So 8^8 over counts the total number of useful combinations and the true number is 15 choose 8, which is 6435 (for details on this math, see: en.wikipedia.org/wiki/Multiset#Counting_multisets ) Support StatQuest by buying my books The StatQuest Illustrated Guide to Machine Learning, The StatQuest Illustrated Guide to Neural Networks and AI, or a Study Guide or Merch!!! statquest.org/statquest-store/
We take for granted all that went behind that idea of 95% CI that you stated - it was Jerzy Neyman's who came up with that definition. Have you read "The Lady Tasting Tea"? A bit of a history of some incredible mathematicians, including Ronald Fisher and Jerzy Neyman. The 95% comes up on page 123. Thanks for all your valuable statistics videos!
So, if we take our sample of 8 observations, and we calculate a 95% confidence interval around the sample mean by bootstrapping, and then a genie appears and tells us that the true population mean lies outside of that confidence interval, that's the same as saying that our original 8-observation sample's mean actually wouldn't appear 95% of the time if we repeated the experiment infinitely many times, each experiment being an 8-observation sampling of the population?
@@alexandersmith6140 The definition is of a 95% CI is that if we repeated the process of creating the 95% CI a ton of times, 95% of the CIs created that way would overlap the true mean. Thus, if collected 8 measurements and used Bootstrapping to calculate a 95%, then that if we repeated that process of creating the 95% CI a ton of times (collected 8 measurements, then calculated the CI with bootstrapping), then 95% of those CIs will overlap the true mean. In other words, it doesn't matter if we use bootstrapping, or some formula to calculate the CI, in both cases we have to collect 8 measurements a ton of times.
I have done a master's in stats and a course in data analysis, and the only reason I've passed these things is that after a long and confusing lecture I can just come and watch you explain it in simple terms. Bam! Thank you so much!!
What I love about you is that you explain the big picture first. You help me understand why we should care in the first place, or the motivation behind the concept. Then you dive into the details afterwards, you make the information more accessible without compromising the technical integrity of the information. A very rare skill indeed, I'm reading Introduction To Statistical Learning in R ( ISLR ) and some chapters aren't intuitive, whenever I read a chapter that doesn't make sense I just watch your videos. That's how I know you're not compromising the technical integrity of the information, because what you say doesn't contradict what I read in academic papers, it's just easier to understand than what I read in academic papers. You are one of a kind!
I think you summed up the value of these videos really well. Starting with the big picture and then zooming into the details is so much more beneficial for learning and I think this is one of the things Josh nails!
Passed all my stats courses already (thanks to your videos for a major part), but I'm still watching these as they come out, lol. Keep it up Josh, this channel is so good.
@@statquest is it really so effective? We really can only be as confident -- that bootstrapping produces characteristic data -- as we are that the sample is representative of the distribution -- right? Unconfident extrapolation seems like a good way to pollute datasets.
I can never get over how your videos make me love statistics when all my professors and recommended texts made me run away from it. Super grateful!! Also, I think I asked when this video was coming about a year ago.
My man sounds sounds excited and bored at the same time and Im here for it 😂 Great explanation, something my Prof couldn’t manage. Elite university my ass lol
You're probably the best guy for this job. Even though I don't know where I'm gonna apply all these. I just keep going through all of your videos. After finishing up this playlist I'll watch the ML playlist. Keep amazing us. Thank you JOSH
All semester long I have been floundering through my statistics class, no thanks to my professors' boring and quite difficult-to-follow lectures on the materials. I've felt so dumb all semester, so when the next section called for "bootstrapping" I finally decided to throw her lecture videos aside and see if someone could explain the concepts better on UA-cam. Boy am I glad I stumbled upon this. The visuals are straight to the point and the way you talk through everything very slowly and clearly is SOOO helpful. The enthusiasm and goofiness helps me keep my attention, which is a pain for me with ADHD. I could rewatch my prof's videos 5 times and retain nothing. Makes me wanna just burst into tears from frustration. But I felt like I could actually keep up with this video and _understand_ it! TL;DR thank you for making this, it was a HUGE improvement over my professor's teaching style and I will DEFINITELY be consulting you for future topics. You're a peach
I read a section on bootstrapping countless times and only understood it finally after watching your video! All I have to say to that is: BAM! (and thanks a bunch)
You sir are an absolute legend. Really helping me getting through my course, because my professor explains the same concept in a method that is 100 times harder to understand
and just like that bam!! i was stuck for the last six hours rewatching what my instructor posted on the portal but this explanation made so much sense and easier to grasp the concept. thank you so much Josh!
Thanks for the videos, embarrassingly I'm relearning a lot of these concepts even though I graduated with a Statistics major. It's coming a lot easier now.
Happens a lot more often than you think. I graduated with a physics major not long ago and I can say I still cannot consider myself a physicist. I constantly keep finding myself learning things from awesome channels like Josh's that I'm supposed to know by now.
@@Synthanicmusic I do as a data scientist. Honestly, If you know that you don't know what you're doing then you are going to be better positioned than most; it means you will be questioning why you are applying certain tests/methods, rather than just doing so blindly. Especially in the workforce you will see a lot of badly reasoned statistics!
Watched the Stanford's and other lectures on similar topics, but you made it really simple and easier to understand. You teach good!! BIG BOOM BAMM !! thanks man
Another great video. This video explains how to do bootstrap, which is the easy part. The more difficult part is to understand why bootstrap works. The conceptual challenge is that bootstrapping assumes that if we were to repeat an experiment, it would produce one of the outcomes we had observed. This could be a huge assumption, depending on the applications. Boot strapping does not add any new information to what has been observed.
"The reason why this works is because the histogram of the sample tends to look very similar to the histogram of the population. That's really the key idea behind the bootstrap, and we will see how this idea can be used in all kinds of complicated situations. " Taking an online course on bootstrap regression and came here to try to understand why bootstrap works when it does not generate any new information.
@@sgpleasure When you sample from a population, it’s unsurprising that the distribution of the sample resembles the distribution of the population. So, you’re not really obtaining any new information. In essence, we’re only pretending it’s new information, when in fact, it’s just reconfirming existing information.
What a comprehensive and fun discussion! I really had trouble understanding the concept of bootstrapping by myself but your lecture helped me a great deal :> Kudos!
Thanks for the great video : ) Just wanted to note that at ~ 8:26 when you are mentioning a bootstrapped median distribution, your x-axis still says Mean Values. I'm sure it's not much of a problem and likely people understand that but thought it was mentioning just in case that someone might get confused!
A professor at the university I studied at was apparently a key contributor to Bootstrapping. Excellent job at explaining it in such an easy-to-understand way!
Thank you so much for your wonderful videos. I have a small request to provide a lecture on FLDA, GMM, EM Algorithm, MLE estimation, MAP estimation. Also, there are some lectures which are not in the book, please also include those lectures too. Thank you so much again!!!. I want to learn more and more from your lectures.
In 8:23 the notation on the x-axis should be median values not mean values since we are using median as statistic measurement for bootstrapping in this case...pls look into it
I'm studying at a top 10 research university in the States and every professor has a PhD from Harvard/Stanford, but none of them teach stats as well as StatQuest 🙃
I went to the second best college in the nation for my degree and many of the students that has been at "better schools" couldn't explain basic chemistry and biology concepts. It's frustrating to feel like it's all for a paper now. I did learn that is what you make of it though.
i dont like stats, but damn, this is so easy to understand and the BAMs Got me laughing HAHHA Tysm Josh! Might made me wanna binge watch some of ur vids and learn stats in my own time damn
The best stats teachings out there! Kudos!!!! Question : Do we need to know/estimate the distribution(normal/gamma/exponential/etc) of the bootstrapping histogram to determine the 95% confidence interval in cases where central limit theorem doesn’t apply( such as median)?
Thank you very much!! One question: When a need to calculate de standard error, I just need to calculate de standard deviation of the resamples? Or a need to calculate the standard deviation divided by the square root of n?
Remember what the standard error is - it the standard deviation of the means we would get from collecting a lot of different samples and calculating the mean for each one. So, if we use bootstrapping to create a bunch of means, all we need to do is calculate the standard deviation of those means..
The purpose of the 95%CI is to tell us whether or not the observed mean, 0.5, is statistically different from 0, and, in this context, when a 95%CI contains 0, we fail to reject the hypothesis that there is a statistically significant difference between the observed mean and 0.
@@statquest hello josh thank you for replying just one more question so whenever the CI contains 0( or the mean we are trying to differentiate from) in it we will fail to reject the null hypothesis correct ?
NOTE: A lot of people ask "What happens when the original collection of measurements is not representative of the underlying distribution?" It's important to remember that a confidence interval is not guaranteed to overlap the true, population mean. A 95% CI means that if we make a ton of CIs using the same method, 95% of them will overlap the true mean. This tells us that 5% of the time we'll be off. So yes, a sample that is totally bonkers is possible, but rare. Understanding this risk of making the wrong decision, and managing it, is what statistics is all about.
Also, at 5:55 I say there are up to 8^8 combinations of observed values and possible means, but this assumes that order matters, and it doesn't. So 8^8 over counts the total number of useful combinations and the true number is 15 choose 8, which is 6435 (for details on this math, see: en.wikipedia.org/wiki/Multiset#Counting_multisets )
Support StatQuest by buying my books The StatQuest Illustrated Guide to Machine Learning, The StatQuest Illustrated Guide to Neural Networks and AI, or a Study Guide or Merch!!! statquest.org/statquest-store/
We take for granted all that went behind that idea of 95% CI that you stated - it was Jerzy Neyman's who came up with that definition. Have you read "The Lady Tasting Tea"? A bit of a history of some incredible mathematicians, including Ronald Fisher and Jerzy Neyman. The 95% comes up on page 123. Thanks for all your valuable statistics videos!
@@natasgestel6873 Yes, I've read the book. Those dues were pretty smart.
Thank you for explaining that order doesn't matter. I was looking for the clarification on this everywhere.
So, if we take our sample of 8 observations, and we calculate a 95% confidence interval around the sample mean by bootstrapping, and then a genie appears and tells us that the true population mean lies outside of that confidence interval, that's the same as saying that our original 8-observation sample's mean actually wouldn't appear 95% of the time if we repeated the experiment infinitely many times, each experiment being an 8-observation sampling of the population?
@@alexandersmith6140 The definition is of a 95% CI is that if we repeated the process of creating the 95% CI a ton of times, 95% of the CIs created that way would overlap the true mean. Thus, if collected 8 measurements and used Bootstrapping to calculate a 95%, then that if we repeated that process of creating the 95% CI a ton of times (collected 8 measurements, then calculated the CI with bootstrapping), then 95% of those CIs will overlap the true mean.
In other words, it doesn't matter if we use bootstrapping, or some formula to calculate the CI, in both cases we have to collect 8 measurements a ton of times.
I have done a master's in stats and a course in data analysis, and the only reason I've passed these things is that after a long and confusing lecture I can just come and watch you explain it in simple terms. Bam!
Thank you so much!!
Thanks! I'm glad my videos are helpful! :)
I am presently in your shoes, taking a Data Science Course but thanks to @statquest. giving him Double Bam!!
That's cool! How are your studies/career going?
There is nobody on UA-cam that explains statistics better or in a more entertaining way than you! Keep it up!
Wow, thanks!
What I love about you is that you explain the big picture first. You help me understand why we should care in the first place, or the motivation behind the concept. Then you dive into the details afterwards, you make the information more accessible without compromising the technical integrity of the information. A very rare skill indeed, I'm reading Introduction To Statistical Learning in R ( ISLR ) and some chapters aren't intuitive, whenever I read a chapter that doesn't make sense I just watch your videos. That's how I know you're not compromising the technical integrity of the information, because what you say doesn't contradict what I read in academic papers, it's just easier to understand than what I read in academic papers. You are one of a kind!
Thank you very much!
@@statquest No, thank YOU Josh!
I think you summed up the value of these videos really well. Starting with the big picture and then zooming into the details is so much more beneficial for learning and I think this is one of the things Josh nails!
@@CaptainFeatherSwordzIt's worlds apart from what the education system has conditioned us to right?
Passed all my stats courses already (thanks to your videos for a major part), but I'm still watching these as they come out, lol. Keep it up Josh, this channel is so good.
Thank you very much! :)
Still floored that this works as a method
I know - it's so easy, yet so effective.
@@statquest is it really so effective? We really can only be as confident -- that bootstrapping produces characteristic data -- as we are that the sample is representative of the distribution -- right? Unconfident extrapolation seems like a good way to pollute datasets.
@@patrickjdarrow Just like any statistical method, you have to have a reasonable sample size. n = 8 as a minimum is a good starting point.
I am justt speechless at - how can you mae something so complicated so simple , hats off to you and thanks a ton
Thank you!
I can never get over how your videos make me love statistics when all my professors and recommended texts made me run away from it. Super grateful!! Also, I think I asked when this video was coming about a year ago.
Glad it finally came out! :) Sorry it takes me so long to make videos.
My man sounds sounds excited and bored at the same time and Im here for it 😂 Great explanation, something my Prof couldn’t manage. Elite university my ass lol
bam!
You're probably the best guy for this job. Even though I don't know where I'm gonna apply all these. I just keep going through all of your videos. After finishing up this playlist I'll watch the ML playlist. Keep amazing us. Thank you JOSH
Thanks!
All semester long I have been floundering through my statistics class, no thanks to my professors' boring and quite difficult-to-follow lectures on the materials. I've felt so dumb all semester, so when the next section called for "bootstrapping" I finally decided to throw her lecture videos aside and see if someone could explain the concepts better on UA-cam. Boy am I glad I stumbled upon this. The visuals are straight to the point and the way you talk through everything very slowly and clearly is SOOO helpful. The enthusiasm and goofiness helps me keep my attention, which is a pain for me with ADHD. I could rewatch my prof's videos 5 times and retain nothing. Makes me wanna just burst into tears from frustration. But I felt like I could actually keep up with this video and _understand_ it!
TL;DR thank you for making this, it was a HUGE improvement over my professor's teaching style and I will DEFINITELY be consulting you for future topics. You're a peach
Hooray! Thank you very much. Just for reference, here's a list of all of my videos: statquest.org/video-index/
@@statquest thank you very much
I read a section on bootstrapping countless times and only understood it finally after watching your video! All I have to say to that is: BAM! (and thanks a bunch)
Hooray!!! :)
You sir are an absolute legend. Really helping me getting through my course, because my professor explains the same concept in a method that is 100 times harder to understand
Happy to help!
and just like that bam!! i was stuck for the last six hours rewatching what my instructor posted on the portal but this explanation made so much sense and easier to grasp the concept. thank you so much Josh!
Bam! Glad it helped!
I wonder n feel so much regard for the institution and teachers, who taught you... no doubt, you are doing an incredible job...stay blessed always
Thank you! :)
Thanks for the videos, embarrassingly I'm relearning a lot of these concepts even though I graduated with a Statistics major. It's coming a lot easier now.
Glad to help!
Don't be embarrassed, it's not your fault, but the education system's
Happens a lot more often than you think. I graduated with a physics major not long ago and I can say I still cannot consider myself a physicist. I constantly keep finding myself learning things from awesome channels like Josh's that I'm supposed to know by now.
I've always grasped well enough to get a good grade but not well enough to embed, so I have to go back a lot.
Thank you
TRIPLE BAM!!! Thank you so much for supporting StatQuest!!! :)
I love SQ, because I finally "get" bootstrapping, despite having used it for years!
BAM! :)
@@Synthanicmusic I do as a data scientist. Honestly, If you know that you don't know what you're doing then you are going to be better positioned than most; it means you will be questioning why you are applying certain tests/methods, rather than just doing so blindly. Especially in the workforce you will see a lot of badly reasoned statistics!
¡Gracias!
Hooray!!! Muchas Grasias for supporting StatQuest!!! BAM! :)
Thanks!
TRIPLE BAM!!! Thank you so much for supporting StatQuest!!! It means a lot to me that you care enough to contribute.
Watched the Stanford's and other lectures on similar topics, but you made it really simple and easier to understand. You teach good!! BIG BOOM BAMM !! thanks man
Thank you! :)
Another great video. This video explains how to do bootstrap, which is the easy part. The more difficult part is to understand why bootstrap works. The conceptual challenge is that bootstrapping assumes that if we were to repeat an experiment, it would produce one of the outcomes we had observed. This could be a huge assumption, depending on the applications. Boot strapping does not add any new information to what has been observed.
Noted
"The reason why this works is because the histogram of the sample tends to look very similar to the histogram of the population. That's really the key idea behind the bootstrap, and we will see how this idea can be used in all kinds of complicated situations. "
Taking an online course on bootstrap regression and came here to try to understand why bootstrap works when it does not generate any new information.
@@sgpleasure When you sample from a population, it’s unsurprising that the distribution of the sample resembles the distribution of the population. So, you’re not really obtaining any new information. In essence, we’re only pretending it’s new information, when in fact, it’s just reconfirming existing information.
What a comprehensive and fun discussion! I really had trouble understanding the concept of bootstrapping by myself but your lecture helped me a great deal :> Kudos!
Glad it was helpful!
You made this concept so much easier to understand than what I was supposed to be learning it from. Thank you so much!!
Glad it was helpful!
Don't be shameless and I introduced your videos to my best classmates as a secret Weapon/Bam to pass the final exam. LOL. Huge help for sure. Thanks.
Thank you!
Thanks for uploading these videos. It takes a lot of time and efforts to make such quality content. Thank you, Sir.
Glad you like them!
BEST explanation EVER of bootstrap. Thanks for your dedication!
Glad it was helpful!
You are a legend my friend! A legend. I am doing my masters in Data Science this fall and this is amazing
You can do it!
Wow this is so good. The intro made me laugh so hard, it wasn't even that funny I just didn't expect it.
Thanks!
i learn by example not just theoretical way. and you do exactly what i need. thank youuuu
Thanks!
Not the information I was looking for but i couldn't stop myself from watching it to the end. It was quite entertaining :)
*BAM
That's awesome! BAM! :)
I am learning machine learning and came to this term , this videos explain it very clear, thank you.
Thanks!
BRO YOU ARE THE BEST, CLEAR VISUAL AND FAST JUST WHAT I NEED NEW SUB!!!!
Thank you!
Always glad to see a new statquest! BAM!
BAM! :)
I'm going to recommend this channel to a bunch of my machine Learning nerds. This guy deserves every hype possible!
Thank you! :)
Wow! this is the first time I learned this. awesome!
BAM! :)
Thanks for the great video : ) Just wanted to note that at ~ 8:26 when you are mentioning a bootstrapped median distribution, your x-axis still says Mean Values. I'm sure it's not much of a problem and likely people understand that but thought it was mentioning just in case that someone might get confused!
Thanks!
One of the most useful video on this topic on youtube, thanks!
Wow, thanks!
blud just dropped one of the best explanatory videos out there and thought we wouldnt notice☠☠☠
bam! :)
Mr. Josh - u are amazing. World needs more ppl like u. Its like education on another level. Thank you
Thanks! :)
This kinda feels illegal xD Really nice explained!
Thank you! :)
This is the most amazing video I've seen on bootstrapping, thank you! Quadruple Bam!
Wow, thanks!
OMG, it was a truly easy-to-understand video! Both the animation, narration, and explanation!!!! I wanna give a billion likes!!!
Wow, thanks!
The work you do is awesome!! Love it.
Thank you!
this is better than college-level advanced course !!! thank you
Wow, thanks!
The intro was gold 🔥
bam!
I can only say one thing: BAM!!! you are the best teacher BAM!!!
Thank you!
Excellent explanation as always by StatQuest!!! Thx a lot!!!
BAM! :)
i loved your bams and the illustrations for the steps and your explanation helped a lot
Thank you!
Best intro ever
Pixar would envy you
bam!
The triple BAM was amazing, thank you!
Thank you!
Are there even comments that you do not comment on?
Very good video, thank you!
Sometimes there are, but it's rare.
Wow, that was super easy to understand. Thank you very much
double bam! :)
bruh... this explanation is simply awesome!
Glad you liked it
So smoothly explained.
Thank you sir.
Thank you!
A professor at the university I studied at was apparently a key contributor to Bootstrapping. Excellent job at explaining it in such an easy-to-understand way!
What's the University and Prof's name?
bam!
Thanks a lot, your video helps me and hopefully it will help my paper too
Thanks!
Thank you so much for your wonderful videos. I have a small request to provide a lecture on FLDA, GMM, EM Algorithm, MLE estimation, MAP estimation. Also, there are some lectures which are not in the book, please also include those lectures too. Thank you so much again!!!. I want to learn more and more from your lectures.
Thanks! I'll keep those topics in mind.
Statquest is the netflix for data science concepts.
bam!
Very clear explanation. Well done!
Thank you! :)
University professor explained it in a confused and insufficient way (to put it politely), then I came to StatQuest.
bam!
Josh, you're sent to us from heaven, thanks
:)
Bootstrapping? More like "Bro, it's awesome knowledge you're dropping!" 👍
Bam! :)
@@statquest Boot! 🥾
Dear Josh I bought a few study guides :) Thanks so much for your videos
Awesome! Thank you so much for your support!!
Big BAM for so much statistic knowledge in such little time
Hooray!
Love the explanation ..... Thank uh soo much❣️
Thanks!
Josh if you need someone who cleans your room or makes the dishes, just give me a call. I own you that
Wow! :)
i read 30 pages of a book , almost get sth,watch 10 min of statquest, fully understand the subject, you're the best bro
Thanks!
In 8:23 the notation on the x-axis should be median values not mean values since we are using median as statistic measurement for bootstrapping in this case...pls look into it
Yep. That's a typo.
Amazing videos, simple and well explained.
Many thanks!
Great way to break bootstrapping into common language.
Thanks!
I'm studying at a top 10 research university in the States and every professor has a PhD from Harvard/Stanford, but none of them teach stats as well as StatQuest 🙃
Thanks! :)
@@bellahuang8522 They already have your money, so they don’t care. College is such a scam
I went to the second best college in the nation for my degree and many of the students that has been at "better schools" couldn't explain basic chemistry and biology concepts. It's frustrating to feel like it's all for a paper now. I did learn that is what you make of it though.
I loved the idea of shameless self promotion idea lol. Thanks for your time and effort.
Thank you! :)
Thank you for the teaching 🎉
Any time!
Amazing, I love when you say Bam 😂
Bam! :)
easy to understand....thanks josh!
Thank you!
Great video!
Btw, you could probably do a really good Solid Snake voice. Would love to get an Easter egg in one of the next videos!!
That would be funny. :)
Awesome! The proofs about it seems to be nice
Thanks!
Love all of your videos!! Thanks a lot!
Glad you like them!
Thank you so much, your videos are always so helpful to me
Glad you like them!
Thank you for explaining... Eat tomato and stay healthy....
bam! :)
i dont like stats, but damn, this is so easy to understand and the BAMs Got me laughing HAHHA
Tysm Josh!
Might made me wanna binge watch some of ur vids and learn stats in my own time damn
Hooray!
9 th wonder I learned bootstrapping and confidence intervals! hurray!
double bam! :)
I love the terminology alert😂
quadruple bam !😂
bam!
You rock Josh. Thanks for making this video!
Thanks!
Nice way of explanation!! BAM!!!
Thanks!
Before this video, I thought bootstrapping was a way of tying your shoe laces, lol
Ha! :)
@@statquest yeah lol :)
Nice explanation...awesome
Thank you!
Thank you so much, very clear again! Not planning to make some videos about Fisher information, Jackknife, and Delta method, by any chance? 😬
No time soon, but I'll keep them in mind.
@@statquest Jackknife would be cool!
The best stats teachings out there! Kudos!!!! Question : Do we need to know/estimate the distribution(normal/gamma/exponential/etc) of the bootstrapping histogram to determine the 95% confidence interval in cases where central limit theorem doesn’t apply( such as median)?
No
@@statquest Thanks! :)
Nice work , man
Thanks!
Great video! I learn so much with you!
Awesome! Thank you!
Thank you very much!! One question: When a need to calculate de standard error, I just need to calculate de standard deviation of the resamples? Or a need to calculate the standard deviation divided by the square root of n?
Remember what the standard error is - it the standard deviation of the means we would get from collecting a lot of different samples and calculating the mean for each one. So, if we use bootstrapping to create a bunch of means, all we need to do is calculate the standard deviation of those means..
@@statquest Ooh I got it! Thank you very much for your answer and for being generous enough to explain! :)
Hello i had a question when you said that the confidence interval contain 0 in it shouldn't it be 0.5 since that is the mean ?
The purpose of the 95%CI is to tell us whether or not the observed mean, 0.5, is statistically different from 0, and, in this context, when a 95%CI contains 0, we fail to reject the hypothesis that there is a statistically significant difference between the observed mean and 0.
@@statquest hello josh thank you for replying just one more question so whenever the CI contains 0( or the mean we are trying to differentiate from) in it we will fail to reject the null hypothesis correct ?
@@ishangrotra7265 That's the idea, however, I believe the null specifically refers to 0.
@@statquest thank you josh please keep up the good work you have of a really great help !
Josh i dont know if u take requests, but i would love a video about lstm, grus, recurrent neural networks in general. Anyways lovely video.
Thanks! I'll keep those topics in mind.
Okay you are the best thank you for doing this video !
Thank you!
Thanks Josh, you are the one!
Thank you and congratulations again. I'm so glad I was helpful. BAM! :)
thank you, this was very helpful
Glad it was helpful!
Ur videos are just so cool, tnx a lot
Glad you like them!
Love it!!! It's really helpful
Thanks!