Total Yeast and Mold Count (TYMC) testing is a cornerstone of cannabis product safety, yet regulatory action limits, testing protocols, and reporting practices vary widely across state markets. This variation has raised questions about the utility of TYMC testing and plating/count-based techniques in general—whether observed results in compliance testing reflect true cannabis product contamination or artifacts of inconsistent regulatory and laboratory practices. Building on prior work establishing that microbial contamination in cannabis cultivation follows a naturally overdispersed growth process, this study benchmarks state-level TYMC testing data against a lognormal (LN) model reflecting the underlying biological assumptions of microbial contamination and propagation. Because the LN model encodes natural constraints on how contamination should be distributed, deviations from the model reveal where testing or reporting practices depart from biologically plausible microbial behavior. Applying this framework to TYMC records from six state cannabis markets, we find that states differ substantially in how well their reported data conform to the LN benchmark. Notably, states with higher regulatory action limits show stronger agreement with the model's natural-growth assumptions, while states with lower limits show patterns consistent with data censoring and other reporting artifacts that obscure the true contamination distribution. These findings suggest that regulatory decisions like the action limit design itself shape lab testing quality and reliability: overly conservative thresholds place undue restrictions on cultivators' ability to operate and sell product while also producing testing data that are harder to validate and interpret, undermining the public health goals they are meant to serve. We propose statistical overdispersion models such as the LN model approach as a practical, data-driven tool regulators and laboratory scientists can use to evaluate and calibrate standard lab sampling protocols and regulations like action limits—improving the reliability of testing programs while preserving robust consumer protection.