This document contains tweets and commentary from an analytics expert discussing A/B testing and optimization. It lists common mistakes in A/B testing like not testing for long enough, peeking at results prematurely, and failing to properly integrate analytics. It also provides advice on prioritizing opportunities, knowing when to stop tests, and the importance of testing whole purchase cycles. Throughout it emphasizes the importance of a collaborative, data-driven approach to optimization.