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Stats and experimentation interviews for data scientist, growth, and product-analytics roles — hypothesis testing, power, p-values, common A/B testing pitfalls (peeking, multiple comparisons, novelty effects), and how to read experiment results in real product contexts.
Sample questions
These are the kinds of questions you'll drill. Real mock rounds will generate many more — tailored to the exact JD you paste in.
Explain what a p-value actually is — and what it isn't.
How do you calculate the sample size needed for an A/B test with an expected 3% lift on a 5% baseline conversion?
Why is "peeking" at A/B test results before the end of the experiment a problem, and what techniques mitigate it?
A test shows a statistically significant 0.5% revenue lift. Do you ship it? Defend your reasoning.
Walk me through how you'd diagnose a Simpson's-paradox style result in segmented A/B data.
Reading Statistics & A/B Testing questions isn't the same as answering them live. You need the rep — the pause, the clarifying question, the moment where you realize you forgot the framework. We put you in that exact spot, then show you what a strong answer would have looked like.
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Paste the JD, pick your topics, and go. Full session, full feedback, no fluff.
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