<p>This article investigates theoretical and practical aspects of one of the urgent metrological issues — the concept of measurement uncertainty for physico-chemical quantities. A hybrid method is developed for selecting and determining the distribution type of a priori information in evaluating type B standard measurement uncertainty, which is a key task in metrological assurance. The relevance of the study lies in the need to improve the reliability of uncertainty assessment under conditions of limited a priori information, as existing approaches often rely on subjective expert judgments, which can lead to inaccurate results. The scientific novelty includes the development of a hybrid method and mathematical models for selecting the distribution of the information base in assessing type B standard uncertainty; the method is based on the combination of the maximum entropy principle and Bayesian theory. The research methodology includes analysis of existing approaches, formalization of metrological criteria for distribution selection, and development of a decision-making algorithm. The adequacy and effectiveness of the method were tested at the Scientific Laboratory of Physico-Chemical Quantities of the “Uzbek National Institute of Metrology” and accredited measurement (testing) laboratories compliant with ISO/IEC 17025:2017. The results can be applied in improving the accuracy of measurement uncertainty assessments and refining metrological control procedures, and are required in ISO/IEC 17025:2017 accredited laboratories, interlaboratory comparison providers under ISO 17043:2023, and international comparisons within the BIPM-KCDB database on calibration and measurement capabilities (CMC). The developed method may be used by scientific centers and laboratories involved in uncertainty evaluation, metrology, and testing. Its implementation will enhance the objectivity of uncertainty evaluation, improve the reliability of expanded uncertainty, and reduce risks associated with type B standard uncertainty.</p>