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.
MediumTechnical
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Medium: Explain Simpson's paradox in a business context. Provide a short synthetic example (two tables or numbers) where aggregate data suggests improvement while segmented data shows decline. Describe how you would investigate and report this to product and leadership.
MediumTechnical
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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.
HardTechnical
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Hard: You maintain forecasts for hundreds of SKUs with intermittent demand and many zero observations. Describe suitable forecasting approaches (including Croston, intermittent-specific methods, or hierarchical models), how you'd evaluate accuracy (which metrics), and how you'd automate model selection at scale.
EasyTechnical
0 practiced
You have a transactions table with schema:| transaction_id (PK) | user_id | amount DECIMAL | occurred_at TIMESTAMP |Write an ANSI SQL query (works in Postgres) that returns, for each user who had at least one transaction in the last 30 days, the total amount, average amount, transaction count, and the timestamp of their most recent transaction. Explain how you handle NULLs and time zones.
MediumTechnical
0 practiced
Medium: Describe the Bonferroni correction and false discovery rate (FDR) control (e.g., Benjamini-Hochberg). When would you apply each in a business analytics context, and what are the trade-offs?
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