The reliability of machine learning critically depends on dataset quality. While machine learning applied to computer vision and natural language processing benefits from high-quality benchmark datasets, cyber security often falls behind, as quality ties to the ability of accessing hard-to-obtain realistic data that may evolve over time. Android is, however, positioned uniquely in this ecosystem due to AndroZoo and other sources, which provide large-scale, continuously updated, and timestamped repositories of benign and malicious apps. Since their release, such data sources provided access to populations of Android apps that researchers can sample from to evaluate learning-based methods in realistic settings, i.e., over temporal frames to account for app evolution (natural distribution shift) and test datasets that reflect in-the-wild class ratios. Surprisingly, we observe that despite this abundance of data, performance discrepancies of learning-based Android malware detectors still persist even after satisfying such realistic requirements, which challenges our ability to understand what the state of the art in this field is. In this work, we identify five novel factors that influence such discrepancies: we show how such factors have been largely overlooked and the impact they have on providing sound evaluations. Our findings and recommendations help define a methodology for curating trustworthy datasets towards sound evaluations of Android malware classifiers.