Abstract

This article compares frequentist and Bayesian reasoning, using practical engineering examples and offers guidance on selecting the best approach—helping quality professionals achieve effective, evidence-based solutions.While frequentist techniques offer strong error controls and objectivity for specific contexts, the author concludes that Bayesian methods deliver superior uncertainty quantification and natural adaptability, making them particularly valuable in most engineering scenarios involving prior knowledge, limited data or evolving contexts.