Experiment Coverage

Experiment coverage is the share of product changes that are instrumented, tested, or released through an experimentation-first flow rather than launched blind.

Key points

• Statsig treats coverage as a core enterprise experimentation lever: when only a small fraction of features are tested, experiments remain optional and are easy to drop under timeline pressure ^[src-036].
• At high coverage, experimentation becomes a release gate: features do not truly ship until data says they are safe or valuable ^[src-036].
• Enterprises struggle with coverage because parallel roadmaps, legacy code paths, and quarterly pressure all encourage “just launch it” behavior ^[src-036].
• Partial coverage creates compounding blind spots: teams over-index on the few things they measure, and leadership may believe incomplete trend lines ^[src-036].
• The article recommends integrating feature flags and experiments so every feature can be a test by default ^[src-036].
• It also recommends aligning engineering KPIs with metric impact rather than feature launch and sunsetting legacy code that cannot be instrumented ^[src-036].

Related entities

• Statsig

Related concepts

• Enterprise-Scale Experimentation
• Feature-Gated AI Code Rollouts
• Parallel A/B Testing
• A/B Test Acceleration
• Outcome-Obsessed Product Management

Source references

• ^[src-036] Yuzheng Sun — “Addressing complexity in enterprise-scale experimentation” (2025-04-23)