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

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