Did the Test Really Win? Significance and Peeking
How to tell whether an A/B test really won: statistical significance, sample size, and the peeking that declares nonexistent winners.
Hypothesis-driven experimentation programs on Optimizely turn behavioural insight into measurable conversion gains, like the +57% lift delivered for Whirlpool EMEA e-commerce. This section covers the methods and the results behind each test.
How to tell whether an A/B test really won: statistical significance, sample size, and the peeking that declares nonexistent winners.
The anatomy of the experimentation program behind the +57% conversion lift on Whirlpool EMEA: how it started, what we tested and what worked.
How to write an A/B test hypothesis that gets results: from behavioural basis to expected metric, the format I use so I don't waste traffic.
A complete guide to CRO and experimentation: from hypothesis to A/B test, prioritisation and significance. The method behind Whirlpool EMEA's +57%.