Why one A/B testing tool withholds winners until the math actually supports it

A software team behind an A/B testing tool has explained why their platform deliberately avoids declaring test winners prematurely, citing the statistical pitfalls of repeated significance checking. In standard frequentist testing, looking at results multiple times inflates the false-positive rate well beyond the intended 5%, potentially reaching 20% or higher with ten checks. To counter this, the tool keeps results labeled as 'Still collecting' until a valid decision boundary is crossed, and always displays confidence intervals alongside sample sizes to prevent misreading early lifts. The platform also surfaces sample ratio mismatch warnings prominently in red, since unequal traffic splits can render all conversion data meaningless. Instead of discouraging frequent monitoring, the team advocates sequential testing methods that mathematically account for continuous observation throughout an experiment's run.