Define hand-waving accuracy and launch decision

## Context You work on a VR product that introduces a new interaction feature called **hand waving** (the system detects when a user is waving their hand and triggers some UI/interaction). Engineering believes it’s ready to launch, but you (as the Data Scientist) are asked to evaluate readiness. ## Task 1. **Define “hand-waving accuracy.”** - What exactly is the unit of evaluation (frame, time window, gesture event, session)? - What labels/ground truth are needed and how would you obtain them? - Which error types matter most (false positives vs false negatives) and why? - What metrics would you report (e.g., precision/recall/F1, latency, stability), and how would you choose thresholds? 2. **Connect model/metric quality to product outcomes.** - Propose a set of **primary**, **diagnostic**, and **guardrail** metrics that tie detection quality to user experience (e.g., engagement, retention, frustration, safety). - Describe potential confounders (novelty effects, device differences, power users vs new users) and how you would handle them. 3. **Data collection and aggregation.** - What events/logs would you instrument? - How would you aggregate the metrics (per event, per user/day, per session) and why? 4. **Diagnostics & visualization.** - Name at least 2 diagnostic cuts (e.g., lighting, device type, hand-tracking quality, motion intensity) and the plots you’d use to identify failure modes. 5. **Launch recommendation & exec reporting.** - What would you present to a VP-level audience as the single most important KPI (plus 2–3 supporting metrics)? - What is your launch decision framework (ship, ship-to-% ramp, delay), and what are the go/no-go criteria?