From the course: Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference

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Hypothesis testing checklist

Hypothesis testing checklist

From the course: Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference

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Hypothesis testing checklist

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- [Speaker] Okay, this is a checklist that I've used for many years as a quick reminder of all the numerous things that a P-Value below 0.05 could mean. And the things that a P-Value above 0.05 could mean. So we'll do a quick review of this, and you'll get a chance to try working with this checklist actually in the upcoming challenge and solution. So when you get a result below 0.05, naturally, the first thing that everyone thinks of, is that we have a true positive. We have an actual effect. And that could certainly be true. But now, let's talk about what else could be going on. Well, even though you might have a significant result, you have to be careful to check for the magnitude of the effect. So what can happen is you can have a result that's statistically significant, but so minimal in its magnitude that it's not helpful very much. I remember years ago, reading a study of the effectiveness of some toothpaste…

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