A/B Testing with Feature Flags: Turning Every Rollout into an Experiment
Blog post from GrowthBook
Feature flags control which users receive a software change and enable rapid rollbacks, while A/B tests use randomized, persistent assignments and concurrent control groups to determine whether a change caused a measurable effect. A percentage rollout alone cannot establish causality because outcomes may be affected by timing, user mix, or other simultaneous changes. Converting a flagged release into an experiment requires a predefined hypothesis, treatment and control variations, randomized sticky assignment, primary and guardrail metrics, and statistical analysis that can detect significance, sample-ratio mismatches, and other data-quality issues. Percentage rollouts are suited to limiting exposure risk, safe rollouts automate staged releases and rollback based on guardrails, and experiments are appropriate when teams need evidence of impact. Recommended practices include logging exposures server-side, validating tracking with A/A tests, expanding experiment coverage without reshuffling existing users, and preventing interference between concurrent tests through coordination or namespaces. The text argues that integrated platforms such as GrowthBook can combine feature management, experimentation, warehouse-based metrics, and statistical reporting to reduce duplicated tools and improve testing workflows.
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