Capital One Analytics & Experimentation interview questions focus on rigorous, business-oriented causal thinking: interviewers evaluate your ability to design clean experiments, choose and defend primary and guardrail metrics, detect bias and interference, and translate statistical results into product recommendations that respect regulatory and risk constraints. Expect a mix of case-style problems (design an A/B test or diagnose a metric shift), technical questions about power, sequential testing, and variance reduction techniques, and hands-on data work using SQL or Python to validate assumptions and compute lifts. For interview preparation, prioritize experiment design fundamentals (hypotheses, randomization, sample-size calculations), common industry methods (CUPED, multiple-testing corrections, always-valid inference), and practical skills like instrumentation checks, data plumbing, and clear stakeholder communication. Practice end-to-end scenarios: define the metric, design the test, run simple analyses, interpret edge cases, and rehearse concise recommendations. Mock interviews with feedback and a few focused coding/data exercises will make your answers both analytically sound and business-ready.