Split testing, or A/B testing, is a crucial tool for companies across diverse industries, primarily utilized for making binary launch decisions on product changes. While often employed for pre-launch experimentation to determine a product's effectiveness, its potential extends far beyond just launch decisions. Testing allows companies to check assumptions, measure long-term feature efficacy, understand contributions to growth, diagnose regressions, and run proof-of-concept tests, among other uses. Properly designed tests help isolate the effects of changes, providing critical insights into user behavior without needing to understand every confounding variable. While statistical rigor is essential in interpreting these tests to avoid false positives, unexpected results can offer valuable opportunities for further investigation and hypothesis development. The practice of testing, though fraught with challenges such as peeking and publication bias, fundamentally revolves around understanding the impact of changes, making it an exciting domain for hypothesis-driven learning and product development.