Despite numerous code comprehension studies, there is no consensus on what code features affect comprehensibility for software engineers. Results in these studies often contradict each other, even using similar methodologies or datasets. These contradictions make it difficult for both researchers and practitioners to derive replicable findings into code comprehension. Most prior studies employ controlled experiments with synthetic code, student subjects, single languages, short methods, and small sample sizes; these design decisions threaten their authenticity and generalizability. In this work, we approach code comprehension with a different set of restrictions: we examine 604k methods, across all method lengths, from real open open-source code systems across five programming languages, but do not control nor observe the environment in which the developers actually comprehended the code. We argue that we can still derive insight into comprehensibility since developers wrote, evolved, and reviewed the source code we examine, and that this means someone found the code comprehensible. Our methodology provides insight into reasons for prior inconsistencies and demon- strates the influence of experimental design decisions on results while providing actionable insights for software engineers who are trying to write comprehensible code. We find that Length—the number of lines of code in a method—heavily correlates with prior comprehension metrics and may be a confounder of prior results. We also find that comment presence serves as a useful comprehension metric: across all languages and projects, comments co-locate with large, complex, and hard-to-read code.