Your question is Frequentist vs Bayesian for Long Experiments. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do you decide between a frequentist and a Bayesian approach for a long-running product experiment?
Discuss how the choice depends on the decision objective, stopping rule, prior information, tolerance for false positives and false negatives, and the operational need for continuously updated results. Explain how you would define the primary metric and guardrails, set an explicit MDE, choose the randomization unit, and pre-register the analysis plan. Address how you would handle peeking, novelty effects, sample ratio mismatch, and a decision where statistical evidence conflicts with practical impact.
