“An orange used car is least likely to be a lemon.” At least that’s what was claimed by The Seattle Times, The Huffington Post, The New York Times, NPR, and The Wall Street Journal. However, this discovery has since been debunked as inconclusive. As data gets bigger, so does a common pitfall in the application of standard stats: Testing many predictors means taking many small risks of being fooled by randomness, adding up to one big risk. The tragic but common mistake is called p-hacking. In this episode, we cover this issue and provide guidance on tapping data’s potential without drawing false conclusions.
"Are Orange Cars Really not Lemons?" by John Elder and Ben Bullard, Elder Research, Inc.: www.elderresearch.com/orange-car
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