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Networking researchers work from mental models of the Internet’s important properties. The scenarios used in simulations and experiments (including our own) reveal aspects of these mental models, often including one or more of the following implicit assumptions: Flows live for a long time and transfer a lot of data. Simple topologies, like a “dumbbell” topology with one congested link, are sufficient to study many traffic properties. Flows on the congested link share a small range of round-trip times. Most traffic across the link is one-way; reverse-path traffic is rarely congested. All of these modeling assumptions can deeply affect simulation and experimental results, and therefore our evaluations of research. But none of them are confirmed by measurement studies, and some are actively wrong. Some divergences from reality are unimportant, in that they don’t affect the validity of simulation results, and simple models help us understand the underlying dynamics of our systems. However, as a community we do not yet understand which aspects of models affect fundamental system behavior, and which aspects can safely be ignored. It is our belief that lack of good measurements, lack of tools for evaluating measurement results and applying their results to models, and lack of diverse and wellunderstood simulation scenarios based on these models, are holding back the field. We need a much richer understanding of the range of realistic models, and of the likely relevance of different model parameters to network performance.