<p>Null results in empirical research are commonly framed as scarce outcomes suppressed by publication bias. This paper argues that the central problem is not the absence of null findings, but the systematic misinterpretation of uncertain evidence as informative effects due to overly narrow conceptions of uncertainty. Drawing on metrology, the science of measurement in the physical sciences, the paper introduces metrological uncertainty as a broader framework for interpreting null results that integrates sampling variability with uncertainty arising from measurement imprecision, model dependence, and contextual factors. From this perspective, many statistically significant findings may overstate evidential strength, while many nonsignificant results remain genuinely uninformative. The paper develops the concept of informative nulls, defined not by the inclusion of zero but by the exclusion of substantively meaningful effect sizes. A three-step inferential framework is proposed—recognize uncertainty, minimize it, and evaluate it against predefined regions of practical equivalence. This approach reframes debates on null results, replicability, and practical relevance, and offers a more transparent and cumulative foundation for marketing research inference.</p>