✎Blog
A/B testing,
explained clearly.
What we end up explaining anyway, written down: runtime, significance, data protection and the mistakes that cost the most money.
Choosing an A/B testing tool: 10 questions before you decide
Feature lists all look alike. These ten questions separate tools you can work with from tools you replace later.
Read more 7 min readPreventing flicker in A/B tests
When visitors briefly see the old version, it distorts more than the impression, it distorts the result. The causes, and what helps.
Read more 5 min read12 A/B test ideas for Shopify stores
Concrete test ideas for product pages, collections and cart, sorted by effort, each with the goal that belongs to it.
Read more 6 min readUnderstanding significance without a statistics lecture
What “95% significant” really means, why the interval matters more than the p-value, and how to spot a false winner.
Read more 9 min readA/B testing and GDPR: what actually matters
First-party instead of third-party, your own server instead of someone else’s data centre.
Read more 8 min readHow long should an A/B test run?
The honest answer: until the sample size you calculated up front is reached, but at least two full weeks.
Read more 7 min readMake your next change an informed one
Less guesswork.
More now we know.
One question is a good place to start.