Articles exploring frameworks through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
5 articles
A/B test results don't live in a vacuum — they get interpreted, reframed, and weaponized by stakeholders with different agendas. Here's how to build a single source of truth that survives organizational politics.
The pressure to prove every test is a winner leads teams to cherry-pick metrics after seeing results. Here's how post-hoc metric shopping destroys credibility — and the one rule that prevents it.
Most A/B testing advice is written by people who've never defended a losing test in a business review. Here are 10 practices that survived real traffic, real org politics, and real budget pressure — from an experimentation lead running 100+ tests a year.
Most experimentation advice assumes perfect statistical significance. Here is how to make the best decision when the data will never be complete — a pragmatic framework from 150+ enterprise experiments.
Most experimentation teams write hypotheses that are really just disguised solutions. Here is why problem-first hypothesis design produces better tests — and how to stop solutionizing before you understand the leak.