Graham McNicoll

Every experimentation leader eventually runs into the result nobody expected. A counterintuitive result lands, the team does not like what it says, and the instinct kicks in. Blame the platform. Blame the data. Rerun it until it says something in line with the expectations or bias. That is how experimentation programs lose credibility inside a company. Not from bad tests. From bad responses to good tests. The defense has to be built before you need it. Run AA tests so you know the platform returns what it should when nothing has changed. Use a system you can audit, where any result can be opened up and traced back to the numbers underneath. If you cannot interrogate a surprising result, you will not trust it. Audit any result end to end at growthbook.io.