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Anirban Deb. “Safely on Muck Rack

Anirban Deb. “Safely

As seen in: Towards Data Science
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Wish Tackles Peeking with Always Valid p-values

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By et al. “Peeking, Mandie Liu, Anirban Deb. “Safely, Applications . IEEE
| Towards Data Science
Peek and conclude experiments adaptively without inflating the false-positive rate Do you estimate the effect size and calculate the sample size before running your A/B test? Do you check the p-value only once when you conclude the A/B test? When the p-value is 5.1% (0.1% higher than the threshold 5%) and the pre-calculated sample size has been reached, instead of running the experiment longer, do you stop the experiment and conclude that the product feature is not improving business?
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