A/B Testing and Experimentation for Growth
A/B Testing and Experimentation for Growth A/B testing helps teams learn what really moves users. By comparing two versions side by side, you can measure which one performs better on a chosen goal. A strong test starts with a clear hypothesis and a single metric to improve, such as signups, clicks, or completed actions. Keep the scope small and the decision rule simple. How to design a test Start with a concrete hypothesis: “A different headline will increase click-through by 7%.” Pick a primary metric and a reasonable duration or sample size. Create variants that differ in only one element to isolate effects. Use random assignment and guard against bias or leakage between variants. Predefine a success threshold (statistical significance) before looking at results. Analyze results with a simple rule: if the primary metric meets the threshold, implement; if not, learn and iterate. Common experiment types ...