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. For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider... Buying my book, The StatQuest Illustrated Guide to Machine Learning: PDF - https://statquest.gumroad.com/l/wvtmc Paperback - https://www.amazon.com/dp/B09ZCKR4H6 Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: null ...a cool StatQuest t-shirt or sweatshirt: null ...buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer 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