We’re going to finish up our discussion of p-values by taking a closer look at how they can get it wrong, and what we can do to minimize those errors. We’ll discuss Type 1 (when we think we’ve detected an effect, but there actually isn’t one) and Type 2 (when there was an effect we didn’t see) errors and introduce statistical power – which tells us the chance of detecting an effect if there is one.
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:
Mark Brouwer, Erika & Alexa Saur Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Nathan Taylor, Divonne Holmes à Court, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, D.A. Noe, Shawn Arnold, Ruth Perez, Malcolm Callis, Ken Penttinen, Advait Shinde, William McGraw, Andrei Krishkevich, Rachel Bright, Mayumi Maeda, Kathy & Tim Philip, Jirat, Eric Kitchen, Ian Dundore, Chris Peters
—
Want to find Crash Course elsewhere on the internet?
Facebook – http://www.facebook.com/YouTubeCrashCourse
Twitter – http://www.twitter.com/TheCrashCourse
Tumblr – http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
Watch Now
