Data Analysis and Insight Generation Questions

Ability to convert raw data into clear, evidence based business insights and prioritized recommendations. Candidates should demonstrate end to end analytical thinking including data cleaning and validation, exploratory analysis, summary statistics, distributions, aggregations, pivot tables, time series and trend analysis, segmentation and cohort analysis, anomaly detection, and interpretation of relationships between metrics. This topic covers hypothesis generation and validation, basic statistical testing, controlled experiments and split testing, sensitivity and robustness checks, and sense checking results against domain knowledge. It emphasizes connecting metrics to business outcomes, defining success criteria and measurement plans, synthesizing quantitative and qualitative evidence, and prioritizing recommendations based on impact feasibility risk and dependencies. Practical communication skills are assessed including charting dashboards crafting concise narratives and tailoring findings to non technical and technical stakeholders, along with documenting next steps experiments and how outcomes will be measured.

EasyTechnical

66 practiced

Using SQL (Postgres), write a concise query to compute overall conversion rate from 'signup' to 'first_purchase' within 30 days. Table schema: events(user_id text, event_name text, event_time timestamp). Define conversion rate as the percentage of unique users who had both a signup and a first_purchase event where purchase_time <= signup_time + interval '30 days'. Assume event_time is correct and each user has at most one 'signup' and one 'first_purchase'.

HardTechnical

47 practiced

You have weekly repeated measures of user engagement for six months and an A/B assignment applied at week 10. Describe how you'd use a mixed-effects (hierarchical) model to estimate the treatment effect over time: specify fixed effects, random effects, how you'd model time and treatment×time interactions, and list key assumptions and diagnostics you'd check.

EasyTechnical

66 practiced

Describe how you would synthesize qualitative usability findings (e.g., user quotes, observed friction points) with quantitative analytics (funnel drops, time-on-task) when both point to a usability issue in the checkout flow. Provide a short example sentence that demonstrates how combined evidence strengthens a recommendation.

EasyTechnical

56 practiced

As a design researcher, explain the steps you would take when starting an exploratory quantitative analysis on a newly collected product event dataset (event-level logs). Include how you'd validate data quality, which initial summary statistics and distributions you'd inspect, and three early hypotheses you might generate to guide follow-up research.

HardTechnical

46 practiced

You observe that average order value (AOV) increased while average items per order decreased, which contradicts domain expectations. Describe a step-by-step diagnostic plan and robustness checks to explain this pattern: include cohort or segment splits, price/promotion checks, product-mix decomposition, instrumentation audits, and placebo or backtest checks you would run.

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