I was working on a feature adoption analysis and I hypothesised that power users would drive long-term retention. My initial numbers seemed to support that, but when I dug deeper I realised the correlation was partly driven by a confounding variable — users who joined during a promotional period. Once I controlled for that, the effect was weaker. I updated my deck to note the limitation and recommended we keep monitoring before making a big investment. My manager appreciated the honesty and we ended up validating the hypothesis six months later.
I hypothesised that reducing onboarding steps would increase seven-day activation by at least fifteen percent. Post-launch analysis showed activation was flat, but session depth dropped eight percent — users were reaching the core feature faster but not understanding it. I killed the rollout within forty-eight hours and rewrote the recommendation: the problem was comprehension, not friction. I partnered with the UX researcher to design a contextual tooltip experiment, which lifted seven-day activation by eleven percent. The original hypothesis was wrong on the mechanism, not the goal — and moving quickly on that distinction is what mattered.