Sequential Testing

Sequential testing provides a statistically valid way to check experiment results before the planned end date. Traditional fixed-horizon testing requires waiting until the full sample size is collected, and looking at results early inflates false positive rates. Sequential methods adjust confidence thresholds to account for multiple looks, maintaining statistical validity while enabling faster decisions.

For growth teams, sequential testing accelerates the experimentation velocity that drives growth. AI-enhanced sequential testing methods like always-valid p-values and confidence sequences allow continuous monitoring without statistical penalties. Growth engineers should implement sequential testing for experiments where speed matters, such as testing major product changes where early detection of harm is critical, or high-traffic features where enough data accumulates quickly. The key benefit is stopping losing experiments early to reallocate traffic to better variants, reducing the opportunity cost of running suboptimal experiences. Teams should understand the trade-offs: sequential methods typically require slightly more total samples than fixed-horizon tests for the same power, but the ability to stop early usually more than compensates. The critical implementation detail is using proper sequential boundaries, since simply checking standard p-values repeatedly invalidates the analysis.