Articles exploring bayesian through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
4 articles
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.
A practical comparison of Bayesian and frequentist A/B testing from a CRO practitioner who's run 100+ experiments. Covers the peeking problem, sequential testing, and a decision table for choosing the right approach.
Optimizely now offers three statistical engines: Sequential (Stats Engine), Frequentist Fixed Horizon, and Bayesian. Each one changes what you measure, how you decide, and when you can stop. Here's how to pick the right one for your team.
Compare Bayesian and Frequentist approaches to A/B testing. Understand the practical differences, when each excels, and why the debate matters less than your fundamentals.