Google Analytics & Experimentation interview questions at Google focus on your ability to turn data into reliable product decisions rather than just produce correct formulas. Expect problems that probe experimental design, metric choice, statistical validity and power, bias and confounding, and the pragmatic tradeoffs of rolling features to real users. Interviewers typically evaluate your causal reasoning, familiarity with A/B testing best practices (including sequential analysis and multiple comparisons), technical fluency with SQL or analysis tools, and the clarity with which you translate numbers into product recommendations. For effective interview preparation, practice end-to-end scenarios: design an experiment, define guarded metrics and guardrails, compute sample size and stopping rules, diagnose surprising results, and explain remediation. Work on clear, concise narratives that justify assumptions and surface uncertainty; rehearse technical fluency with SQL queries and small reproducible analyses in Python or R. Simulated post-mortems of real experiments and timed whiteboard explanations of metric design will pay off, as will framing answers around user impact, measurement limitations, and next steps rather than only statistical significance.