Advanced Data Analysis and Statistics Questions
Focuses on higher level analytical and statistical techniques for interpreting data and testing hypotheses. Topics include time series analysis, cohort and segmentation analysis, correlation and causation distinctions, descriptive versus inferential statistics, experimental design and hypothesis testing, consideration of sample size and power, detection of confounding variables including Simpson s paradox, and practical interpretation of results and limitations. Emphasizes choosing appropriate methods for given questions and communicating statistical findings clearly.
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Hard: Provide a reproducible plan to detect Simpson's paradox in a dataset of product conversions across multiple countries and devices. Include SQL or pandas steps to compare aggregate vs stratified metrics, guidelines for determining whether stratified or aggregated insights should guide decisions, and how to present both views to stakeholders.
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Medium: Explain multicollinearity in multiple regression. How would you detect it in a dataset and what remedies would you apply? Discuss how multicollinearity affects interpretation vs prediction.
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Medium: Describe the difference between parametric and non-parametric hypothesis tests. Give two examples of each and explain when a non-parametric test is preferable in product analytics.
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Hard: You suspect concept drift in a live churn prediction model: predictive performance degraded over 6 months. Outline the steps to investigate drift (data, label, feature distributions), quantify its impact on metrics, and approaches to mitigate drift (retraining cadence, online learning, feature engineering).
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Hard: Describe a Bayesian approach to analyzing an A/B test for conversion, including choice of prior, computation of posterior probability that variant B is better than A, and how to report results to stakeholders unfamiliar with Bayesian stats. Discuss pros and cons versus frequentist tests.
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