Bootstrapping Main Ideas!!!

Bootstrapping is one of the simplest, yet most powerful methods in all of statistics. It provides us an easy way to get a sense of what might happen if we could repeat an experiment a bunch of times. It turns point estimates into distributions that can be used to calculate all kinds of stuff, including standard errors, confidence intervals and even p-values. In this video, we show how it can be used to calculate standard errors and confidence intervals. In Part 2, we'll see how to calculate p-values.

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0:00 Awesome song and introduction
2:18 Bootstrapping in action!
4:23 Bootstrapping defined
6:40 Calculating standard errors and confidence intervals with bootstrapping
8:13 What makes bootstrapping so awesome

Correction:
5:55 8^8 combinations of observed values and possible means 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

#StatQuest #Bootstrap #Statistics

[statquest]

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.

[yannivdp]

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!!

[NeuralNine]

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

[NTesla]

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.

[haloforgeguy]

Still floored that this works as a method

[monoarul_islam_]

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

[Hemzees]

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.

[gamuchiraindawana]

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!

[aishah]

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

[rhexieleelafuente]

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!

[abhishekpawar]

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

[aneishajay]

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)

[KillerFridge]

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

[diegobuenovillafane]

BEST explanation EVER of bootstrap. Thanks for your dedication!

[qilin_prance]

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

[nautical]

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.

[luko]

Mr. Josh - u are amazing. World needs more ppl like u. Its like education on another level. Thank you <3 !

[AvatardSwag]

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

[hugoviotti]

Always glad to see a new statquest! BAM!

[yuvrajkhanna]

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