For an experimentation program to yield tangible business results, understanding what to test, how to test it, and how to analyze results is crucial. Excessive or misguided testing can waste resources and undermine stakeholder confidence. The aim should be to conduct strategic experiments that deliver cumulative marginal wins, focusing on when to experiment, choosing appropriate tools, and translating outcomes into actionable insights. High-impact experiments are characterized by high uncertainty, conflicting opinions, random audience exposure, and measurable outcomes. It is equally important to recognize when not to experiment, such as when changes are obviously beneficial, low-risk, time-sensitive, or lack adequate data. A well-structured experimentation lifecycle includes maintaining a backlog of ideas, designing experiments with clear metrics, and iterating on findings. Choosing the right tools involves options like standard experimentation, bringing your own analysis, or a hybrid approach. Effective decision-making relies on flexible statistical methods and consistent dashboards, while data slicing can enhance experiment value by identifying subgroup preferences. Building a strong experimentation practice requires discipline, streamlined processes, and a focus on high-uncertainty, high-impact changes.