Data Interpretation & Dashboard Literacy Questions

Practice interpreting data visualizations, trend lines, and metric dashboards. Develop ability to identify what's noteworthy (seasonality, anomalies, correlations) vs. normal variation. Think about causation vs. correlation. Practice explaining what a metric trend means in business terms and what actions it might suggest.

HardTechnical

0 practiced

Describe methods a PM can use to detect and prevent p-hacking or multiple testing fallacies when running many dashboard-filtered analyses and experiments. Be specific about tools, processes, and guardrails.

EasyTechnical

0 practiced

You're asked to define a product success metric for a new free-to-paid funnel (onboarding to subscription). Propose a clear, measurable metric (name, numerator, denominator, timeframe), explain why you chose it, and how you would instrument it in analytics.

MediumTechnical

0 practiced

You ran an A/B test for a new onboarding flow. Variant B shows a 3% lift in activation with p=0.07 and 80% power targeted. How would you interpret this result to stakeholders? Discuss statistical and practical significance, what additional analyses you would run, and whether you would recommend rollout.

EasyTechnical

0 practiced

You're asked to validate whether a reported 15% week-over-week increase in purchases is real. List three statistical or data checks you would run (e.g., sample size, duplicate transactions, seasonality adjustments) and explain what each check would reveal.

EasyTechnical

0 practiced

You're reviewing a product dashboard showing Daily Active Users (DAU) for the last 180 days as a line chart. The chart shows a steady upward trend for ~120 days, then a sudden 20% drop over three days, followed by a return to the previous trend. As a product manager, how would you interpret these patterns in business terms? What immediate data checks and stakeholder actions would you take to investigate the drop and prevent future incidents?

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