The text critiques existing prioritization frameworks for A/B testing, such as PIE, ICE, and PXL, for their reliance on subjective judgment and overemphasis on effort, arguing that the true strength of A/B testing lies in its ability to remove guesswork from understanding user behavior. Growth Book offers a new framework centered on an objective Impact Score to guide prioritization, which evaluates tests based on three components: metric coverage, experiment length, and metric importance. Metric coverage assesses the extent to which an experiment can influence metric conversions, factoring in both visibility and targeting rules. Experiment length estimates the duration needed to achieve statistical significance, incorporating a minimum detectable effect to account for the magnitude of changes. Metric importance assigns a weighted value to metrics based on their organizational significance. The Impact Score, calculated on a 0-100 scale, aims to prioritize tests that can quickly yield significant impacts on critical metrics, enabling product managers to better allocate resources and coordinate with other business activities without relying on conjecture.