This is the kind of question where you can easily go wide and shallow and they'll just keep poking until you fall apart.
Start by clarifying the goal: define what 'improved user engagement' means for the energy dashboard, then propose a randomized controlled experiment with a clear hypothesis, primary and guardrail metrics, and a power analysis to determine sample size and duration. Walk through the statistical tests you'd use, how you'd monitor for novelty effects, and how you'd interpret results to make a launch decision.
Pro tip: Emphasize practical constraints like network effects or seasonality in energy usage, and propose running the test for at least two full weeks to capture weekly patterns. Also mention that you'd pre-register the analysis plan to avoid p-hacking.
Translate 'improved user engagement' into measurable metrics (e.g., daily active users, session duration, frequency of dashboard visits) and specify a primary metric. Include guardrail metrics like page load time or user satisfaction to ensure no negative impact.
Randomly assign users to control (old dashboard) and treatment (new dashboard) groups, ensuring proper randomization unit (e.g., user-level) and avoiding contamination. Consider stratification by key user segments (e.g., heavy vs. light energy users).
Conduct a power analysis to calculate required sample size based on minimum detectable effect (MDE), significance level (α=0.05), and power (1-β=0.8). Use historical variance to estimate. Duration should cover at least two full weeks to account for weekly seasonality and novelty effects.
Use appropriate statistical tests (e.g., t-test for continuous metrics, chi-square for proportions) and check for practical significance. Monitor guardrail metrics and segment-level results. If primary metric improves significantly without harming guardrails, recommend launch; otherwise, iterate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.