Why Bayes? A Decisions First Framework for Business Data Science

Music City Center 

Many data scientists and executives fall into the trap of blindly following Null Hypothesis Significance Testing (NHST) without understanding its limitations. This "null ritual" can lead to misguided business decisions. In 2016, the American Statistical Association warned against reducing scientific inference to mechanical rules like "p < 0.05," noting this can lead to poor decision-making.

This presentation proposes a "Decisions First" framework that prioritizes business objectives over rigid statistical procedures. By adopting a Bayesian perspective, we can treat data analysis as a continuous learning process, estimate decision-relevant probabilities, and properly acknowledge uncertainty.

The framework guides users through defining decisions, formulating data-driven questions, designing appropriate studies, and presenting findings transparently. Rather than seeking absolute certainty, it emphasizes aligning research with business goals and embracing the inherent uncertainty in real-world decision-making. This approach helps avoid common pitfalls and leads to more effective use of data.