Frequentist vs. Bayesian Approaches in Managerial Decision Making: A Critical Perspective

Abstract

In an era of data-driven decision-making, businesses and managers increasingly rely on statistical models to guide strategic choices. The debate between Frequentist and Bayesian approaches in statistical inference has significant implications for managerial decision-making. While both paradigms offer valuable insights, understanding their philosophical foundations, strengths, and limitations is crucial for making informed, evidence-based decisions in complex business environments. A well-informed choice between these approaches can enhance predictive accuracy, optimize resource allocation, and minimize decision-making risks. As businesses face growing uncertainty and competition, leveraging the right statistical framework can provide a crucial strategic advantage in achieving long-term success.