Bootstrapping Main Ideas!!!

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  • Опубликовано: 23 дек 2024

Комментарии • 674

  • @statquest
    @statquest  3 года назад +126

    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 book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/

    • @natasgestel6873
      @natasgestel6873 2 года назад +1

      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!

    • @statquest
      @statquest  2 года назад +3

      @@natasgestel6873 Yes, I've read the book. Those dues were pretty smart.

    • @PastryDonut
      @PastryDonut 2 года назад +1

      Thank you for explaining that order doesn't matter. I was looking for the clarification on this everywhere.

    • @alexandersmith6140
      @alexandersmith6140 Год назад

      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?

    • @statquest
      @statquest  Год назад +2

      @@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.

  • @yannivdp1600
    @yannivdp1600 Год назад +164

    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!!

    • @statquest
      @statquest  Год назад +1

      Thanks! I'm glad my videos are helpful! :)

    • @BheezHandle
      @BheezHandle Год назад

      I am presently in your shoes, taking a Data Science Course but thanks to @statquest. giving him Double Bam!!

    • @PunmasterSTP
      @PunmasterSTP 9 месяцев назад

      That's cool! How are your studies/career going?

  • @NeuralNine
    @NeuralNine 2 года назад +45

    There is nobody on RUclips that explains statistics better or in a more entertaining way than you! Keep it up!

  • @gamuchiraindawana2827
    @gamuchiraindawana2827 Год назад +11

    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!

    • @statquest
      @statquest  Год назад +1

      Thank you very much!

    • @gamuchiraindawana2827
      @gamuchiraindawana2827 11 месяцев назад +1

      @@statquest No, thank YOU Josh!

    • @CaptainFeatherSwordz
      @CaptainFeatherSwordz 9 месяцев назад +3

      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!

    • @gamuchiraindawana2827
      @gamuchiraindawana2827 9 месяцев назад

      @@CaptainFeatherSwordzIt's worlds apart from what the education system has conditioned us to right?

  • @haloforgeguy453
    @haloforgeguy453 3 года назад +165

    Still floored that this works as a method

    • @statquest
      @statquest  3 года назад +16

      I know - it's so easy, yet so effective.

    • @patrickjdarrow
      @patrickjdarrow 3 года назад +22

      ​@@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.

    • @statquest
      @statquest  3 года назад +15

      @@patrickjdarrow Just like any statistical method, you have to have a reasonable sample size. n = 8 as a minimum is a good starting point.

  • @who-n7e
    @who-n7e 3 года назад +115

    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
      @statquest  3 года назад +6

      Thank you very much! :)

  • @Hemzees
    @Hemzees 3 года назад +7

    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.

    • @statquest
      @statquest  3 года назад +2

      Glad it finally came out! :) Sorry it takes me so long to make videos.

  • @monoarul_islam_3
    @monoarul_islam_3 3 года назад +17

    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

  • @meanyapickles
    @meanyapickles Год назад +1

    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 RUclips. 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

    • @statquest
      @statquest  Год назад +1

      Hooray! Thank you very much. Just for reference, here's a list of all of my videos: statquest.org/video-index/

    • @meanyapickles
      @meanyapickles Год назад +1

      @@statquest thank you very much

  • @aneishajay7949
    @aneishajay7949 Год назад +1

    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)

  • @satkarkarki248
    @satkarkarki248 Месяц назад +1

    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!

    • @statquest
      @statquest  Месяц назад

      Bam! Glad it helped!

  • @bellahuang8522
    @bellahuang8522 11 месяцев назад +14

    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 🙃

    • @statquest
      @statquest  11 месяцев назад +1

      Thanks! :)

    • @dan_pal
      @dan_pal Месяц назад

      @@bellahuang8522 They already have your money, so they don’t care. College is such a scam

    • @Portfelio
      @Portfelio 22 дня назад

      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.

  • @잠자기장인-x3r
    @잠자기장인-x3r 8 месяцев назад +1

    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

    • @statquest
      @statquest  8 месяцев назад +1

      Happy to help!

  • @Esmeralda_Art
    @Esmeralda_Art 10 месяцев назад +5

    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

  • @bubblebath2892
    @bubblebath2892 4 месяца назад +1

    I am justt speechless at - how can you mae something so complicated so simple , hats off to you and thanks a ton

  • @SP-qk6vd
    @SP-qk6vd 10 месяцев назад +1

    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

    • @statquest
      @statquest  10 месяцев назад +1

      Yep. That's a typo.

  • @nautical1078
    @nautical1078 3 года назад +18

    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.

    • @statquest
      @statquest  3 года назад

      Glad to help!

    • @vladimirbriceno6051
      @vladimirbriceno6051 3 года назад +2

      Don't be embarrassed, it's not your fault, but the education system's

    • @nancygonzalez3193
      @nancygonzalez3193 3 года назад +4

      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.

    • @ankiwikman1218
      @ankiwikman1218 2 года назад

      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.

  • @aishah8398
    @aishah8398 3 года назад +11

    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

  • @KillerFridge
    @KillerFridge 3 года назад +30

    I love SQ, because I finally "get" bootstrapping, despite having used it for years!

    • @statquest
      @statquest  3 года назад +9

      BAM! :)

    • @KillerFridge
      @KillerFridge 3 года назад +7

      @@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!

  • @ayenisedaghat4667
    @ayenisedaghat4667 9 месяцев назад +1

    You are a legend my friend! A legend. I am doing my masters in Data Science this fall and this is amazing

    • @statquest
      @statquest  9 месяцев назад

      You can do it!

  • @travelthetropics6190
    @travelthetropics6190 3 года назад +6

    Wow! this is the first time I learned this. awesome!

  • @diegobuenovillafane869
    @diegobuenovillafane869 Год назад +3

    BEST explanation EVER of bootstrap. Thanks for your dedication!

  • @leonelemiliolereboursnadal6966
    @leonelemiliolereboursnadal6966 2 года назад +1

    ¡Gracias!

    • @statquest
      @statquest  2 года назад

      Hooray!!! Muchas Grasias for supporting StatQuest!!! BAM! :)

  • @yuvrajkhanna2819
    @yuvrajkhanna2819 8 месяцев назад +1

    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

  • @bushraw66
    @bushraw66 6 месяцев назад +1

    Thank you

    • @statquest
      @statquest  6 месяцев назад

      TRIPLE BAM!!! Thank you so much for supporting StatQuest!!! :)

  • @vtphan2012
    @vtphan2012 3 года назад +33

    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.

    • @statquest
      @statquest  3 года назад +1

      Noted

    • @sgpleasure
      @sgpleasure 2 года назад +1

      "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.

    • @vtphan2012
      @vtphan2012 2 года назад +5

      @@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.

  • @witchaponkitthaworn5998
    @witchaponkitthaworn5998 Год назад +1

    I am learning machine learning and came to this term , this videos explain it very clear, thank you.

  • @benjaminelkrieff6414
    @benjaminelkrieff6414 2 года назад +1

    Thanks!

    • @statquest
      @statquest  2 года назад

      TRIPLE BAM!!! Thank you so much for supporting StatQuest!!! It means a lot to me that you care enough to contribute.

  • @dutonic
    @dutonic 2 года назад +1

    Wow this is so good. The intro made me laugh so hard, it wasn't even that funny I just didn't expect it.

  • @SILOETTE100page
    @SILOETTE100page 4 дня назад +1

    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!

  • @tomatocultivator9539
    @tomatocultivator9539 2 года назад +9

    Thank you for explaining... Eat tomato and stay healthy....

  • @Hilzoti-白狼龙
    @Hilzoti-白狼龙 Год назад +1

    You made this concept so much easier to understand than what I was supposed to be learning it from. Thank you so much!!

  • @SrChengLX
    @SrChengLX 2 месяца назад +1

    BRO YOU ARE THE BEST, CLEAR VISUAL AND FAST JUST WHAT I NEED NEW SUB!!!!

  • @sevenindigo5945
    @sevenindigo5945 Год назад +1

    I can only say one thing: BAM!!! you are the best teacher BAM!!!

  • @studyselection2881
    @studyselection2881 2 года назад +2

    Are there even comments that you do not comment on?
    Very good video, thank you!

    • @statquest
      @statquest  2 года назад

      Sometimes there are, but it's rare.

  • @ZC-xe3wr
    @ZC-xe3wr 16 дней назад +1

    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.

  • @quorrexnoway9127
    @quorrexnoway9127 3 года назад +38

    This kinda feels illegal xD Really nice explained!

  • @НосокНесуществующий

    The intro was gold 🔥

  • @abhishekpawar921
    @abhishekpawar921 3 года назад +9

    Thanks for uploading these videos. It takes a lot of time and efforts to make such quality content. Thank you, Sir.

  • @alessandrobucci951
    @alessandrobucci951 2 года назад +1

    One of the most useful video on this topic on youtube, thanks!

  • @hugoviotti9795
    @hugoviotti9795 3 года назад +3

    Always glad to see a new statquest! BAM!

  • @AliasNichtVerfügbarDrölf
    @AliasNichtVerfügbarDrölf 2 года назад +1

    Not the information I was looking for but i couldn't stop myself from watching it to the end. It was quite entertaining :)

  • @rhexieleelafuente4271
    @rhexieleelafuente4271 2 года назад +19

    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!

    • @statquest
      @statquest  2 года назад +1

      Glad it was helpful!

  • @Acampandoconfrikis
    @Acampandoconfrikis 3 года назад +1

    Best intro ever
    Pixar would envy you

  • @everyhinghere1377
    @everyhinghere1377 Год назад +1

    this is better than college-level advanced course !!! thank you

  • @oscarrosalescorzo
    @oscarrosalescorzo Год назад +2

    The work you do is awesome!! Love it.

  • @punditgi
    @punditgi 3 года назад +4

    Very clear explanation. Well done!

  • @andreyele3464
    @andreyele3464 Год назад +1

    Josh, you're sent to us from heaven, thanks

  • @yohanessatria2220
    @yohanessatria2220 2 года назад +1

    bruh... this explanation is simply awesome!

  • @w157-p5x
    @w157-p5x 10 месяцев назад +1

    blud just dropped one of the best explanatory videos out there and thought we wouldnt notice☠☠☠

  • @ignasialemany7738
    @ignasialemany7738 2 года назад +1

    The triple BAM was amazing, thank you!

  • @keepcontinue
    @keepcontinue Год назад +1

    Excellent explanation as always by StatQuest!!! Thx a lot!!!

  • @davusieonus
    @davusieonus 3 года назад +1

    i loved your bams and the illustrations for the steps and your explanation helped a lot

  • @dashsingh30095
    @dashsingh30095 3 года назад +1

    Statquest is the netflix for data science concepts.

  • @НосокНесуществующий

    Wow, that was super easy to understand. Thank you very much

  • @etwasverfehlt2397
    @etwasverfehlt2397 2 года назад +3

    Josh if you need someone who cleans your room or makes the dishes, just give me a call. I own you that

  • @adityarazpokhrel7626
    @adityarazpokhrel7626 2 года назад +1

    So smoothly explained.
    Thank you sir.

  • @thomasporritt8323
    @thomasporritt8323 3 года назад +1

    Great way to break bootstrapping into common language.

  • @hermesavurquijo9961
    @hermesavurquijo9961 Год назад +1

    Thanks a lot, your video helps me and hopefully it will help my paper too

  • @utkarshsingh2675
    @utkarshsingh2675 2 года назад +1

    easy to understand....thanks josh!

  • @annaspitczok9471
    @annaspitczok9471 2 года назад +1

    Big BAM for so much statistic knowledge in such little time

  • @AvatardSwag
    @AvatardSwag 3 года назад +3

    This is the most amazing video I've seen on bootstrapping, thank you! Quadruple Bam!

  • @estevaodias6404
    @estevaodias6404 3 года назад +1

    Awesome! The proofs about it seems to be nice

  • @dejanspasovic377
    @dejanspasovic377 Год назад +1

    Amazing videos, simple and well explained.

  • @Tozziz
    @Tozziz 2 года назад +1

    Dear Josh I bought a few study guides :) Thanks so much for your videos

    • @statquest
      @statquest  2 года назад

      Awesome! Thank you so much for your support!!

  • @williamss4277
    @williamss4277 8 месяцев назад +1

    Thank you for the teaching 🎉

  • @luko141
    @luko141 2 года назад +1

    Mr. Josh - u are amazing. World needs more ppl like u. Its like education on another level. Thank you

  • @ishangrotra7265
    @ishangrotra7265 Год назад +1

    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 ?

    • @statquest
      @statquest  Год назад

      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.

    • @ishangrotra7265
      @ishangrotra7265 Год назад

      @@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 ?

    • @statquest
      @statquest  Год назад

      @@ishangrotra7265 That's the idea, however, I believe the null specifically refers to 0.

    • @ishangrotra7265
      @ishangrotra7265 Год назад +1

      @@statquest thank you josh please keep up the good work you have of a really great help !

  • @alaminmustafa8902
    @alaminmustafa8902 3 года назад +2

    I love the terminology alert😂
    quadruple bam !😂

  • @jaundersousa3530
    @jaundersousa3530 3 года назад +2

    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!

    • @aditya4974
      @aditya4974 3 года назад +1

      What's the University and Prof's name?

    • @statquest
      @statquest  3 года назад

      bam!

  • @namitaratnakar_6591
    @namitaratnakar_6591 2 года назад +1

    Love the explanation ..... Thank uh soo much❣️

  • @musefaredi5540
    @musefaredi5540 Год назад +1

    Nice way of explanation!! BAM!!!

  • @jasonvoreseeas6578
    @jasonvoreseeas6578 3 года назад +4

    I'm going to recommend this channel to a bunch of my machine Learning nerds. This guy deserves every hype possible!

  • @GeerathBhat-vm1vp
    @GeerathBhat-vm1vp Год назад +1

    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.

    • @statquest
      @statquest  Год назад

      Thanks! I'll keep those topics in mind.

  • @Mesye_bober
    @Mesye_bober 5 месяцев назад +1

    Nice work , man

  • @thepresistence5935
    @thepresistence5935 2 года назад +1

    9 th wonder I learned bootstrapping and confidence intervals! hurray!

  • @AnalyticsAlchemy-nq3kq
    @AnalyticsAlchemy-nq3kq 9 месяцев назад +1

    Nice explanation...awesome

  • @brettclark3885
    @brettclark3885 2 года назад

    At 6:40 when you start to discuss the 95% CI; I think there will be a lot of people who wont understand the subtlety of this distribution. You have created a distribtuion of 'statsitics'; in this case the mean. So, as you would appreciate you have derived the "sampling distribution' of the mean, from which the standard deviation = the standard error of the mean and the 95% CI calcaution is trivial. The uninitated might not appreciate how this is different from a distribution of a single data set; whereby the standard error = the standard devation / sqrt(n).

  • @Observe411
    @Observe411 2 года назад +1

    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!!

    • @statquest
      @statquest  2 года назад +1

      That would be funny. :)

  • @PunmasterSTP
    @PunmasterSTP 9 месяцев назад +1

    Bootstrapping? More like "Bro, it's awesome knowledge you're dropping!" 👍

  • @tsuchiko_7155
    @tsuchiko_7155 2 года назад +4

    OMG, it was a truly easy-to-understand video! Both the animation, narration, and explanation!!!! I wanna give a billion likes!!!

  • @thomasdelossantosverrijp8117
    @thomasdelossantosverrijp8117 2 года назад +1

    thank you, this was very helpful

    • @statquest
      @statquest  2 года назад

      Glad it was helpful!

  • @YK-jn2kp
    @YK-jn2kp 3 года назад +1

    I loved the idea of shameless self promotion idea lol. Thanks for your time and effort.

  • @fgfanta
    @fgfanta 9 месяцев назад +1

    University professor explained it in a confused and insufficient way (to put it politely), then I came to StatQuest.

  • @gitaecookiemonster2892
    @gitaecookiemonster2892 3 года назад +1

    Love all of your videos!! Thanks a lot!

  • @RobertaBraga_
    @RobertaBraga_ 2 года назад +1

    Thanks Josh, you are the one!

    • @statquest
      @statquest  2 года назад

      Thank you and congratulations again. I'm so glad I was helpful. BAM! :)

  • @alexyang2002
    @alexyang2002 2 года назад +1

    really clear, thanks

  • @lahaale5840
    @lahaale5840 2 года назад +1

    Thank you so much, your videos are always so helpful to me

  • @JosephElfassi
    @JosephElfassi 2 года назад +1

    Super clear, thanks!

  • @ElSamyto
    @ElSamyto Год назад +1

    Okay you are the best thank you for doing this video !

  • @nikhilravichandran4302
    @nikhilravichandran4302 2 года назад +1

    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)?

  • @victorbossard9
    @victorbossard9 3 года назад +1

    do you agree that this method is prone to have high bias? If results are biased (due to experimentor for instance), then you'll be concluding something potentially wrong.
    So I feel like it's strange to tell boostrap can replace many experiments.

    • @statquest
      @statquest  3 года назад

      The bias is dependent on the original sample size. So, bootstrapping probably isn't a great idea if you only have a few measurements to begin with. But if you have a fair number, then it has been shown to work very well.

  • @marlocosta5531
    @marlocosta5531 2 года назад +1

    Thank you JOSH!

  • @arifmemovic3383
    @arifmemovic3383 2 года назад +1

    You rock Josh. Thanks for making this video!

  • @rajatbadade20
    @rajatbadade20 3 года назад +1

    Please upload videos on monte carlo simulation and integration

    • @statquest
      @statquest  3 года назад

      I'll keep that in mind.

  • @mahdiranjbar3569
    @mahdiranjbar3569 2 года назад +1

    Ur videos are just so cool, tnx a lot

  • @vig9737
    @vig9737 2 года назад +1

    Josh is on Spotify! BAM

  • @SwapperTheFirst
    @SwapperTheFirst 3 года назад +2

    Thanks! Couple of questions - could someone please clarify this for me, please:
    1) At 8:40 we should see "median values" at the bottom distribution instead of "mean"? 2) also, at the same time mark, why confidence levels moved to the left this far? they cover mostly "feeling worse" data points.
    More general question - is Bootstrapping theoretically or conceptually linked to the Central Limit Theorem?

    • @statquest
      @statquest  3 года назад +3

      1) Oops! That's a typo. It should say "median".
      2) The CI was found by identifying the 2.5% and 97.5% quantiles, which were shifted as seen in the video.
      3) I do not think so.

    • @SwapperTheFirst
      @SwapperTheFirst 3 года назад

      @@statquestThanks, Josh! Could you please elaborate on the CL for medians. _Why_ it is so shifted to the left, compared to CL for mean values. I'm so sorry to bother, but it seemed that I _get_ it, while in reality I cannot understand why the CL for median values is so, so different from CL for means.
      I've purchased your PCA guide. Pure awesomeness!

    • @statquest
      @statquest  3 года назад

      @@SwapperTheFirst Thank you for supporting StatQuest!!! As I wrote earlier, the CI was found by identifying the 2.5% and 97.5% quantiles (95% of the quantiles are between 2.5 and 97.5). If that doesn't make sense to you, consider watching the StatQuest on quantiles: ruclips.net/video/IFKQLDmRK0Y/видео.html

    • @phelipe2587
      @phelipe2587 3 года назад

      I was thinking about Central Limit Theorem.. The sample data comes from some unknow distribution, so if we generate a new dataset and calculate the mean over and over again.. the histogram of these means will be like a normal distribution? If I'm not wrong, that's what central limit theorem is about, right? Unless it doesn't work when you repeat bootstrap like 1,000 or 10,000 times.. i don't know, I'm confusing

    • @SwapperTheFirst
      @SwapperTheFirst 3 года назад

      @@phelipe2587 This is my thought exactly. Using bootstrapping (random process) we get a normalized distribution (for example, of means), even when initial distribution is not normalized.
      I want to make a small experiment, though. I will get data from Josh deck (8 datapoints) and will run the bootstrap, say 10K, using highly random data (say, from random.org). Then I will get 8 datapoints from some other distribution, which is not normal (say, wealth distribution in US) and again, compare with bootstrap distro after 10K.
      Also want to check the median CL in bootstrapped distro, since I (alas) still don't get it.
      But when you play with actual data, instead of endless theories - sometimes you may have an insight.

  • @DurjoyRoy-ll4zt
    @DurjoyRoy-ll4zt 5 месяцев назад +1

    Loved it... Big BAM!

  • @yingyanhuang1688
    @yingyanhuang1688 2 года назад +1

    You are amazing! Thank you!