Bayesian statistics has a reputation for being mathematically intimidating. But the core ideas are surprisingly intuitive, and they map directly onto how good decision-makers already think.
The key insight
Traditional statistics asks: “If my hypothesis is true, how surprising is this data?” Bayesian statistics asks a more useful question: “Given this data and my existing knowledge, what should I believe?”.
This is exactly how experienced executives operate. You don’t treat every new piece of information as definitive. You weigh it against what you already know.
What this means in practice
You get honest uncertainty. Instead of a single number (“the effect is 5%”), you get a range (“there’s a 90% chance the effect is between 2% and 8%”). This makes risk assessment possible.
Prior knowledge matters. If you’ve seen a hundred pricing tests and they usually move revenue by 1-3%, a model that predicts 50% should raise red flags. Bayesian models can encode this experience.
Small data is fine. You don’t need millions of data points. Bayesian methods work well with limited data because they can lean on prior knowledge when evidence is thin.
A common objection
“Isn’t it subjective to include prior beliefs?”
Yes, but that’s a feature, not a bug. Every analysis involves assumptions. Bayesian methods force you to make those assumptions explicit and testable, rather than hiding them in modelling choices.
Why this matters for decisions
Bayesian thinking is ultimately about integrating all the information available to you and asking the right questions. It brings together data, domain expertise, and structured reasoning into a single coherent framework. That makes it uniquely suited to the kinds of decisions commercial leaders actually face: complex, high-stakes, and informed by far more than a single dataset.