15Marketing

A/B Test Reader

Calls statistical significance, flags peeking risks, kills inconclusive tests early — so the 40-60% of marketing tests that get treated as definitive but are actually noise stop driving real budget decisions.

The problem

Marketing teams declare A/B test winners on day 4 of a 14-day test, peek at the dashboard six times before the run completes, and ship the variant that happened to be ahead at the moment they checked — an artifact of multiple-comparisons inflation, not real signal-to-noise. 40-60% of marketing test results are statistically inconclusive when read correctly, but get rolled into the next quarter's plan as "we proved this works." Without a sober reader sanity-checking against the pre-registered hypothesis, sample size, and minimum-detectable-effect, the team optimizes against noise and the real lift opportunity gets pruned in favor of the lucky variant.

Typical leak: 40-60% of A/B test conclusions are statistical noise treated as truth; 5-15% of expected lift surrendered to false-winner deployment

Test-decision accuracy + compounded lift capture

40-60% reduction in false-winner deployments; 15-25% lift recapture from re-running underpowered tests with proper sample size

Optimizely 2024 experimentation benchmark; Stanford SE.18 sequential testing research

Integrates with

MixpanelAmplitudePostHogGoogle Analytics 4HeapHubSpotLookerNotionClaudeSlackn8n

How it works

Agent · A/B Test Reader

Test summary loaded

Variant B · +12% signups

Significance calc · 89%

Peeking risk flagged · 3 peeks