Detection and Quantification Challenges in Microplastics Research: A Statistical Overview

Detection and quantification of microplastics are undermined by the absence of appropriate methods in some contexts—such as a statistically principled LOD for particle-count data—but more pervasively by the uncritical application of ostensibly simple rules outside the conditions under which they are valid. This paper examines the statistical and study-design decision points where such failures most commonly occur and articulates principled constraints on valid inference at each step. Specifically, it (1) distinguishes exploratory from confirmatory research and advocates preregistration to prevent HARKing; (2) argues for field-blank-based limits to improve internal validity; (3) shows how multiple simultaneous comparisons against the LOD inflate the family-wise error rate and how to adjust α accordingly; (4) demonstrates that the LOD multiplier kD depends on blank sample size and critiques fixed-multiplier heuristics lacking statistical justification; (5) examines the consequences of distributional misspecification for LOD estimation; (6) demonstrates that an LOD for summed polymer concentrations exists only under restrictive conditions; (7) clarifies why subtracting limits from individual measurements is not quantification, and that cohort-level inference requires a median comparison via log-transformed data with a corresponding confidence interval; and (8) introduces a Bayesian framework for particle-count LODs that accounts for partial filter inspection. These discussions are summarized in a minimum reporting checklist designed as an evaluative aid—not a prescriptive recipe—to help researchers make analytical choices explicit and support reviewers in assessing methodological validity.