Confidence Intervals: Intro to bootstrapping proportions

For the full context of this lesson (practice and other bootstrap confidence interval videos) see sites.google.c....

[@enxx2362]

The best explanation I could find in youtube

[@BeaverMonkey]

9 years later, this video holds up well. Thanks for a clear explanation!

[@saadati]

After watching 6 other videos about bootstraping, this one was by far the best. Thanks

[@Miriyum1031]

My tuition money should be going to you. Thank you!

[@sumaiyaszone7696]

Sir, Thanking you would not be enough. Pray for the best for you

[@m0hsinsajjad]

I wanted to understand bootstrapping and I studied for 45 mins on the internet but all in vain, now by this I got the whole idea. Thankyou very much

[@traceyhuntley1853]

A beautiful demonstration of the Feynman technique .... And you actually explained WHY we were doing it to begin with rather than just the technique. Thanks heaps

[@GuitarBob311]

This was fantastic!! I’m going over bootstrapping in my BIOSTATS class and this cleared up so much

[@HazemAzim]

Brilliant way to introduce Bootstrapping concept ... Chapeau

[@drpindoria]

Very nice presentation on bootstrapping. Thanks for sharing.

[@youthf7c343]

You are a wonderful teacher! Thankyou for this amazing video

[@satyasruti1219]

I have gone through around 15 websites before coming here. Thanks so much for the crystal clear explanation. I am now 100% confident that i have understood it right

[@IvCastilla]

... Nice explanation with example...!!! I was trying to understand how bootstraping work and how to preform the calculations for several days but NO ONE is so clear and to the grain. Thank you.

[@kashvinivini2264]

you deserve a medal for this buddy!

[@adenforst230]

By far the most intuitive explanation on KZbin

[@md.sabbirahmed7494]

Best explanation among all videos regarding bootstrapping. It is due to giving a real life example. Thank you.

[@arpittyagi6404]

Best explanation I came across.

[@kinslychibs9356]

Thanks a lot. You've made this so easy to understand

[@md.masumbillah8222]

thanks well explained. I appreciate your effort to make this easy explanations.

[@kevinlambert1952]

Well done! Thank you for this very clear explanation!

[@RaviGuptaOnGoogle]

Beautifully explained! Thanks..

[@PetStuBa]

11 minutes have never past so fast .; and that's because it was so damn well explained .. respect !! greetz from belgium :)

[@chandvr]

Awesome explanation. Thanks.!

[@empaulstube6947]

thank you! best explanation of what Bootstrapping is all about

[@soonmi8278]

That was such a great explanation. Thank you!!

[@priyankrajsharma]

i don't have words to say thanks, it was so interesting and no one needs caffeine :)

[@_Vortex___]

Awesome, No-BS . Thank You Keep it up.

[@biswajitbhowmick2689]

Best explanation

[@souravsha]

Very good explanation. Thanks man !

[@aminazgol3918]

I have a presentation about bootstrapping tomorrow and I couldn't guess it is such a cool subject. Tank u for this amazing video

[@sumitkumar-el3kc]

You are a great teacher, really well prepared lecture.

[@torrecuso]

Fantastically clear and fun explanation. Thanks a lot

[@firstkaransingh]

Excellent explanation 👌

[@JimOC]

Great explanation - well done!

[@VesqVj]

I was studying this from a very good and well written book and kept missing the point. You did it better, thanks! :)

[@eyeeuzziah392]

Well explained, thanks.

[@benjamintrinh7824]

Very clear explanation 👍

[@eyescreamcake]

Much clearer than other explanations I've seen. However, "Having said that, an XX% confidence level does not mean that for a given realized interval there is an XX% probability that the population parameter lies within the interval!"

[@asmitsachan202]

The content delivery was absolutely amazing. Keep up the good work @apethan

[@doctari1061]

Good explanation. Thanks

[@epictoxic384]

Great explanation ! Thnx!

[@kattavia92dva]

best video EVER you sir are terrific

[@tausal1]

Amazing explanation! Thank you so much.

[@preetikharb8283]

Well explained! I laughed so hard when he showed the slide of Brad's fault :D :D

[@taianeramos6594]

Thanks for the explanation. I finally understood why my supervisor asked me to do that haha

[@urvashiprajapati4200]

Best explanation thanks

[@AndyWicks]

Clear, concise and informative - thank you. Would it be possible to add the links to the previous/next videos to the video description (or maybe even a list of the videos in a topic?). Thanks - excellent work. :-)

[@nashwahammoud4076]

great explanation

[@m0hsinsajjad]

very easy to understand

[@arnab6476]

still best video for bootsrap

[@ishakabdi9554]

thank you it was really clear

[@harishrallabandi4263]

Thank you so much!! THE BEST EXPLANATION

[@uhyanyan]

This is a great video explanation.

[@saharkhawatmi660]

Very nice explanation. Thanks.

[@eliteimmobilier4621]

U r veey funny 😂 best teacher ever

[@JonathanEtitoO]

Wow, so good explanation!

[@jennyfu720]

thanks for this video

[@nano7586]

Finally understood it.. thank you!

[@avanraws77]

Great explanation! Good job!

[@rasmusarnlingbaath6704]

Love it! Thanks for a great video!

[@zainabkhan2475]

Stats, physics mathematics, they are beautiful!

[@nomihadar]

thanks, a very good explanation

[@lanascribe]

I can finally stop crying and eating too much chocolate! thankyou!

[@sanjaykrish8719]

Excellent!!

[@mallool]

Cool explanation man! I like this video :) Thank you!

[@TimothyMoananu]

Thanks bro. This is super helpful

[@emilieranberg]

very nice tutorial. Thank you :)

[@ylazerson]

great video!

[@stefanozakher7807]

very good job mate

[@mikoajgala2644]

Very cool explanation. Thanks for the video. Regardless of the results, dogs always win.

[@girishewoorkar1907]

superb

[@thepunisher1951]

hell of an explanation, doesn't feel like 12 minutes at all

[@vitolomele]

Super! Just a double check, if you can. In your specific example, the pet survey, I think you could also calculate the confidence interval analytically, without resorting to bootstrapping. As (stdev of the sample mean) = (stedev of the 40 original samples) / SQRT(40). Is that right? I appreciate you picked this simple example for teaching purposes, but I want to double check that bootstrapping is actually superfluous here (while it is very useful for more complex cases, where you cannot easily calculate the statistics analytically). Thank you (and sorry in advance in case I got it all wrong and I'm just waisting your time 😌)

[@andypethan8213]

That is correct -- on any sample you could use either a simulation/bootstrap approach or a normal curve/analytical approach, but the normal curve really breaks down when your basic assumptions for normal curve-friendly samples are not met (with proportions, usually when you get close to either 0% or 100%). However, unless someone is preventing you from using a computer to do your job, just bootstrapping everything is faster and easier. The exception is probably when dealing with confidence intervals of means, you have a sample that is a bit wonky, and you have strong reason to believe that the population looks more normally distributed than your sample -- then you're basically "overriding" the sample data to match a model, which has some validity once in a while.

[@wendya2309]

This was cool!

[@tahmidabtahi]

very helpful

[@santosh8471]

So nicely explained however why do we bootstrap same sample everytime to assume fake population ,why not different random samples of 40

[@SDSMint]

Why not just approximate with normal distribution, z values and the standard error (p*(1-p)/n)̂1/2? What's the advantage I'm not seeing?

[@asd-uv2it]

great

[@xiao6322]

Thanks a lot

[@lubdhakmondal8744]

ThE_EnD for searching more video on bootstrapping

[@MrStensnask]

Btw, could you perhaps make a video on Bremer-support values? I think you'd be good at explaining that too.

[@zainabkhan2475]

Sir I have a question, why did you use the bootstrap instead of CLT though the sample size was 40? Regards from India!

[@alexamurray1800]

Thanks so much this video was really helpful. I do have one question that was kind of addressed in the comments, but not quite. You mention "copying and pasting" the original sample to create a sort of mock population that you can then resample from. From most resources that I've found, the resample method is just resampling with replacement from the original sample. So I'm wondering if the "copy-paste" part was just to sort of conceptually explain the idea of using the original sample as a best estimate of the true population, or if it should be taken more literally. (A lot of the other resources I've found on this are a bit vague/confusing, which is why I'm not sure how to interpret this.) I.e. If I'm making making a bootstrap program in python for a cs class, would I simply resample with replacement from the original dataset? Or would I concatenate the original dataset with itself many times to create a mock population, then resample from that with replacement, each resample being the size of the original dataset?

[@rockychat3]

Alexa Murray You are exactly right -- copy-paste is a conceptual way to think of sampling with replacement from the original sample (since a normal sample does not use replacement but does have a much larger population). For your simulation, just sample with replacement.

[@akhil832]

U R AWESOME! XD

[@sbch179]

Very great tutorial, thank you! I've got a question on the bootstrapping procedure though: Instead of multiplying your sample of 40 elements to represent your population and then iteratively select 40 elements a high number of times, would it be conceptually wrong to leave your original sample of 40 elements, and iteratively select eg 20 elements a high number of times and do the analysis the same way? The difference here is htat your new sample size will be smaller (ie 20 instead of 40 elements). Am I right? Thank you very much!! Simone

[@lucassanchez9108]

sbch179 Hi there, the answer is "not quite." The size of your new samples needs to be equivalent to the size of the original sample, or else the confidence interval will not be the same. One way of maintaining the sample size is to draw from your original sample "with replacement", that is, you could get the same element twice in your new sample. This is equivalent to "copy and pasting" your original sample to the size of the population, like he did in this video. Hope this helped!

[@apethan]

+sbch179 This is a *hair* late to the party, but for the benefit of future viewers, it is important to use the same sample size for each resample. In a small sample, there will be more variation between samples, and thus suggest a larger confidence interval. A set of larger samples will have less variation amongst themselves. As an extreme example, imagine a sample of 5. Being 1 off of the expected value of 60% gives you 40% or 80% with no chance of being somewhere closer. With a sample of 500, there are many ways to be less than 1% away from 60%. This is the logic for why larger samples make better predictions of population values.

[@destrolock1234560]

nice explanation also Peno is definetely cuter

[@JohnDoe-uw5mj]

I thought I invented this "bootstrap" method.

[@quarkzy4133]

how can you ask people which ones cuter over the phone when they can't see it.

[@sohandandekar8976]

Thank you Sir. I have one doubt if you'll please help me. Are there any alternatives to Statkey? How reliable are the results of Statkey?

[@andypethan8213]

Statkey is just running a basic simulation (sample without replacement) from the data you feed it. Nothing too fancy -- I had a high student build their own version of the calculations in a few weeks as a project. The magic of their tool is the fantastic visuals and ability to study a single resample at a time -- fantastically built (so is their curriculum if your school does that).

[@MrStensnask]

I think I love you.

[@subasurf]

fucking amazing

[@chandvr]

hi, the site mrpethan.com seems to be offline.

[@rebeenali917]

nice

[@zaimahbegum-diamond1660]

dam you Brad...

[@oscarlekovic8105]

gg homie

[@ludifexoeasymail2736]

if anyone is looking for the "Startkey" link for a little experiment then: www.lock5stat.com/StatKey/ Don't stop learning

[@OriginalJoseyWales]

I get what you have done but I am concerned about some aspect of this idea. The bootstrapped proportion has no link to your research question. It could be 40% prefer the dog; 40% prefer a particular mobile phone etc. The bootstrap outocme would be the same irrespective of what is being considered.

[@Indrius]

OriginalJoseyWales why do you think it matters if it's a dog or a phone? You are dealing with estimation of proportion here, it makes no difference of what.

[@apethan]

+Indrius Right -- the entire purpose of bootstrapping is to estimate the spread of a sampling distribution of a given sample size. If you were dealing with a quantitative variable, the values from the original sample would probably be more unique than yes/no data and the calculations would be more "personalized" to the problem, if you want to call them that.

[@richardbarton9076]

Great video! But the kitty is definitely cuter...

[@PhotonVision]

Am I the only one not hearing any sound? (while I do with other KZbin videos) Really hard to follow.

[@spicebeats4486]

aye