The Experiment Decision Framework (EDF) is designed to aid experimenters in making informed decisions about their experiments by automating the determination of when sufficient data has been collected and recommending actions based on predefined business criteria. By setting Target Minimum Detectable Effects (MDEs) and decision criteria upfront, the framework addresses issues such as false positives and inconsistent decision-making due to business pressure or personal bias. It provides automated status updates indicating whether an experiment should continue, be altered, or be concluded based on statistical precision and predefined goals. The EDF is available for Pro and Enterprise customers and integrates with existing experiment data and statistical engines, allowing for customizable settings per experiment while maintaining a structured approach to decision-making without altering the experiment's operational process.