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 pvalues. 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 pvalues.

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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

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