Interpreting A/B test results: false negatives and power

This post details the statistical concepts of false negatives and power in A/B testing, explaining their relationship and how to increase power. It covers increasing effect size (bold hypotheses, testing new product areas), sample size (more units, fewer groups), and reducing metric variability. The post uses coin-flipping analogies to illustrate these concepts and their impact on detecting true differences between treatment and control groups.