Before You Launch Your AB Test

A/B tests fail most often before they start — through unclear hypotheses, weak instrumentation, or misaligned success criteria. This course looks at how to design experiments that answer real product questions, define measurable outcomes, and avoid false positives. It emphasizes disciplined thinking about causality and risk so teams can invest in the right experiments. Best for PMs and growth practitioners running experimentation programs who want more reliable signals.