Trigger testing is a technique used in A/B experiments to focus on user actions that are specifically relevant to an experiment, such as clicking a button or scrolling to a particular part of a webpage, ensuring that the data captured is accurate and contextually relevant. This method helps eliminate the noise in data by including only those who have had the opportunity to be influenced by the experiment, thus providing clearer insights and more reliable results. Platforms like Split, which feature an in-app decision engine, facilitate this process by allowing real-time, local decision-making without network dependency, ensuring privacy and reducing performance penalties. This approach contrasts with platforms that require network calls, which can complicate trigger testing and potentially lead to less precise results and a slower user experience. Split promotes a culture of experimentation by providing tools that enable product development teams to confidently release impactful features and continuously improve through data-driven insights.