p-Hacking your A/B tests

Founder takeaway

Treat a “successful” A/B test as evidence to validate, not automatic proof that one variant is better. Build experimentation discipline around distinguishing real signal from false positives.

Why it matters

Acting on noisy test results can send product and growth resources toward changes that do not create durable value, while giving the team false confidence in its learning system.

Relevant guides

Put it to work

Review your recent winning tests: which decisions would you change if the result failed to hold up in a follow-up test? Choose the highest-impact one to validate before investing further.