Bootstrap and Confidence Intervals

In this video, I explain how to make a confidence interval with bootstrapping. Here, we go over how to make a confidence interval with the true population, how to apply bootstrap to get the confidence interval and finally, I walk you through what happens to the confidence interval as the sample size increases and decreases. Enjoy!

Try answering these comprehension questions to further grill in the concepts covered in this video:

1. What is the central limit theorem? And what is the classic way of making confidence intervals?
2. When can we apply bootstrap confidence intervals?
3. What is the sampling distribution and why is it special?
4. Is it reasonable to assume that our sampling distribution will be symmetric?
5. What happens to the sampling distribution as the original sample size increases? As the number of samples to create it increases? And what happens to the bootstrap sampling distribution?
6. What is annoying about the first bootstrap confidence interval that was taught?
7. Can you show empirically that the percentile confidence interval works? Code it.
8. Is there a way that you can estimate the percent of times your confidence interval will be right without the population distribution?

[mohamedgibril]

very helpful! saved hours of research! and your explanation is simple and straightforward! great job and keep uo the informative material!

[davidthiwa]

Great video!! Do you have notes on the bias corrected accelerated (BCA) implentation ?

[eudaimonian]

Does the bootstrap distribution converge to the (hypothetical) sampling distribution? Or is this a separate thing entirely?

[nityasaxena]

What if we know CI of a variable and we use that variable to calculate another. Will same method apply to find CI of new variable?