This question evaluates skills in experimental design and analytics, covering offline counterfactual replay, interleaving and A/B testing, sample-size and power computation, sequential testing and alpha spending, guardrail monitoring and ramp policies, proxy metrics and covariate adjustment, heterogeneous treatment effect analysis, and governance concerns such as p-hacking and Simpson’s paradox within the Analytics & Experimentation domain for Data Scientist roles. It is commonly asked to probe proficiency in rigorously validating ranking changes while balancing statistical error, operational risk and bias mitigation, and it emphasizes practical application of applied statistical concepts and experiment governance rather than purely theoretical understanding.
You are introducing a new ranking algorithm for the home page. You must validate it safely and rigorously using a staged approach:
Be concrete about experiment unit, bucketing, sample size, guardrails, sequential testing, novelty/carryover/seasonality mitigation, ramp policy, proxy metrics and covariate adjustment, heterogeneous treatment effects (HTE) with multiple-testing control, and governance against p-hacking/Simpson’s paradox.
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