Process discovery involves the construction of process models to describe real-world systems, allowing study and improvement of systems based on their data footprints. One quality criterion of discovered models is model-system generalization, which assesses how well a model describes both seen and unseen processes of the system. When the system itself is unknown, event logs must be used to determine model-system relationships, such as generalization. Here we investigate event log representativeness, which measures how well an event log represents its generative system, exploring the extent to which representativeness affects the accuracy of generalization estimation. Our focus is on a bootstrap approach, adopting a simple approximation for log representativeness that correlates strongly with previous measures. Extensive experiments show that log representativeness substantially affects generalization estimation accuracy: highly representative logs can directly represent the system for measuring model generalization, while less representative logs require additional estimations. We also provide insights into bootstrap generalization estimation: reasonable assumptions on the process discovery technique allow the bootstrap method to yield more accurate estimates of generalization for model-system precision than for model-system recall.