The Confidence Tier Model: How to Decide When Your Data Isn't Enough
Meta description: Most testing programs are built for traffic they don't have. Three confidence tiers — proven, directional, speculative — each with its own bet-sizing rule.
TL;DR
Fixed-sample A/B testing assumes you can wait for statistical significance. Most teams can't — traffic is too thin, or the market is moving too fast to wait.
The fix isn't lowering your standards. It's replacing the binary "significant / not significant" gate with three explicit confidence tiers — Proven, Directional, Speculative — each with its own evidence bar and its own bet-sizing rule.
