False Positives vs. False Negatives in Science and Statistics (Type 1 and Type 2 Error)

Statistical tools are amazing at helping us understand the world around us. They let us understand the relationship between important economic indicators, rigorously test the efficacy of new medical interventions, and even let us know, once and for all, which cat video is considered the cutest! But on a more serious note, statistical tests, for all their sophistication can be wrong. And they can be wrong in two very specific ways.

Welcome to Data Demystified. I’m Jeff Galak and in this episode we’ll learn all about the two ways statistical tests can be wrong, what we call Type 1 and Type 2 error, or, in much more reasonable language, False Positives and False Negatives. If you stick around, I’ll provide an intuitive definition of both types of errors using rich examples to help make the ideas stick, discuss what might cause each type of error, and end with a take on which type of error is worse.

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