Articles exploring experiment analysis through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
3 articles
Traditional segmentation requires you to hypothesize which user groups matter. AI flips this by discovering segments from the data itself, revealing hidden audiences where your experiment results diverge dramatically from the overall average.
Understand what p-values really mean in A/B testing, why common interpretations are wrong, and how to use statistical significance correctly for business decisions.
A practical guide to the Bayesian vs Frequentist debate in A/B testing, why it matters less than you think, and what practitioners should actually focus on when choosing a statistical approach.