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Many junior data scientists set statistical power to 80% without understanding what it actually represents. Power is the probability of rejecting the null hypothesis when the alternative hypothesis is true. In this tutorial, I calculate the required sample size for 80% power in an A/B test, then run 10,000 experiments drawing from populations with those exact parameters. The result? We get statistically significant findings in roughly 80% of iterations. This demonstrates what power means in prac
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