Explain Central Limit Theorem's Importance in A/B Testing
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Last updated
Evaluates understanding of the Central Limit Theorem and confidence intervals in A/B testing. Strong answers explain sampling distributions, standard error, normal approximations for experiment metrics, caveats for dependence and heavy tails, and compute the 95% confidence interval as [2.902, 3.098].
Scenario
General statistics knowledge check during analytics interview.
Question
State the Central Limit Theorem and explain why it matters for A/B testing. 2. Given a sample mean of 3, known population standard deviation of 1, and n = 400, construct a 95% confidence interval for the mean. 3. Sketch the sampling distribution on a whiteboard and mark the confidence bounds.
Hints
CLT: sampling distribution of the mean approaches normal; CI = mean ± 1.96*σ/√n.
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