Problem Definition and Hypothesis Formation Questions

Break down ambiguous business questions into specific, answerable analytics problems and define what success looks like. Ask clarifying questions about business context, constraints, stakeholder expectations, and acceptance criteria. Use structured diagnosis and root cause analysis to isolate where a problem occurs by segmenting users, products, time periods, or geographies. Generate multiple testable hypotheses that explain observed outcomes, distinguish correlation from causation, and prioritize hypotheses by likelihood, potential impact, and ease of validation. Frame measurable metrics for each hypothesis and propose high level validation approaches or experiments to confirm or reject the hypotheses.

EasyTechnical

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Explain the difference between success metrics, health metrics, and guardrail metrics for an AI product. Give two concrete examples of each for a personalized recommendation system and explain how each category affects hypothesis acceptance.

EasyTechnical

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Given the events table with schema below, write a SQL query to compute 7-day retention rate: percent of users who performed any event on their cohort day (day 0) and again within 7 days. Show output columns cohort_date, cohort_size, retained_count, retention_rate. Explain assumptions about timezone and users with multiple first events.

Table schema:

sql

events(
  user_id bigint,
  event_type varchar,
  occurred_at timestamp
)

EasyTechnical

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Describe a simple quantitative framework to prioritize multiple hypotheses based on likelihood, potential impact, and ease of validation. Explain how to score each dimension numerically and combine them, and provide a short example ranking for three hypothetical causes.

HardTechnical

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Propose a statistical approach using Bayesian decision theory or expected value of information to prioritize which hypotheses to test under resource constraints. Explain how to compute the expected value of an experiment, incorporate the cost of testing, and present trade-offs to non-technical stakeholders with a simple numeric example.

EasyTechnical

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List common data quality checks you would perform on datasets before using them to validate an analytics hypothesis. For each check briefly explain why it matters and give one simple query or test you could run to detect the issue.

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