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Most A B tests are evaluated the same way. A variation outperforms the control, conversion rate increases, and the test is declared a success. The result is shipped, documented, and added to a growing list of “wins”. On paper, this looks like progress. In reality, many of these wins are misleading. The core issue is not with experimentation itself, but with how success is defined. When tests are judged primarily on conversion rate, they optimize for immediate action, not long

For most companies, A B testing exists at the edge of the business. It is treated as a tactical layer, something that improves conversion rates incrementally, occasionally producing small wins that look good in reports but rarely change the trajectory of growth. That framing is fundamentally limiting. At scale, experimentation is not about optimization, it is about decision making under uncertainty. The difference is subtle but critical. When teams approach testing as a way t