This question evaluates experiment design, causal inference, and metrics-driven decision-making skills within Analytics & Experimentation for Data Scientist roles, covering hypothesis formulation, selection of a primary success metric and guardrails, and the use of manipulation checks such as video share of impressions.
Context: You ran a 14-day A/B test that increases the share of video pins in Home Feed by +10 percentage points to boost engagement. Evaluate hypotheses, define success and guardrails, interpret results, and make a ship/ramp/stop decision.
| Metric | Control | Treatment | Lift vs Ctrl | p-value | 95% CI |
|---|---|---|---|---|---|
| CTR (clicks/impressions) | 4.70% | 4.85% | +0.15 pp | 0.040 | [+0.01, +0.29] pp |
| Saves per impression | 0.92% | 0.87% | -0.05 pp | 0.090 | [-0.11, +0.01] pp |
| Avg session time | 12.0 m | 12.1 m | +0.8% | 0.200 | [-0.3%, +1.9%] |
| Session crash rate | 1.20% | 1.32% | +0.12 pp | 0.010 | [+0.03, +0.21] pp |
| Exposed users | 200,300 | 199,700 | — | SRM p=0.62 | — |
Note: The goal was to increase the video share in Home Feed by +10 pp. Include a manipulation check for “video share of impressions” in analysis.
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