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But here we go!
Scholars testing a research hypothesis have to be worried about two kinds of errors. Type I is failing to reject a false hypothesis. The hypothesis is wrong, but it looks right to you. That’s a false positive.
Type II is rejecting a true hypothesis incorrectly. In fact, your hypothesis is correct, but your data suggests that it’s false. That’s a false negative.
Which one is which?
You’re welcome everybody!
Here’s as far back as I could trace the source. If anyone knows where this came from, please let me know. :)Lisa Wade, PhD is a Visiting Scholar at Tulane University. She is the author of American Hookup, a book about college sexual culture; a textbook about gender; and a forthcoming Introduction to Sociology text. You can follow her on Twitter, Facebook, and Instagram.