Bayesian updating
Start with a prior. Revise it incrementally as evidence arrives.
Thomas Bayes's theorem describes the mathematically correct way to change your mind. Start with your prior belief about how likely something is. As new evidence comes in, update the probability in proportion to how diagnostic that evidence is.
The math is precise. The intuition is what matters in practice: don't throw out your prior beliefs every time new information arrives, but don't ignore the new information either. Update proportionally.
Most thinking errors are non-Bayesian in one of two ways. People either anchor too hard on their prior and refuse to update (see [inconsistency-avoidance]) or they swing wildly with each new piece of evidence (overreacting to recent vivid data).
For operators, the practical Bayesian habit: when forming a view, state the prior explicitly, then state what evidence would change your mind by how much. Then watch the evidence honestly and revise.
Examples in the wild
A leadership team that already believes "the product is great" will resist contradicting evidence (customer churn, missed sales) unless they're explicitly trained to Bayesian-update. The prior is sticky.
Good investors keep written theses about each holding, with explicit "what would change my mind" criteria. When the data triggers those criteria, they update. Most investors don't, which is why exits are often too slow.
Friendships and judgments of people typically don't update enough. You decided someone was "that kind of person" five years ago and your prior is doing all the work. Bayesian re-examination is rare.
Bayesian updating is one of the mental models we apply through real cases inside the Pareto MBA — a part-time program for professionals who want to think clearly about business.