Most models give you a number. The harder question is usually how much to trust it.
I’m a data scientist with nine years of experience building statistical models that answer both — the estimate, and how confident anyone should be in it. That means being explicit about which parts of a result are solid, which are guesswork, and what would change the answer.
It’s a habit that comes from a scientific background: black holes, gravitational wave astronomy, and more recently microscopy, where a result you can’t put honest error bars on isn’t a result. It matters more than it used to. There is a great deal of data around, a great deal of AI, and a corresponding abundance of confident answers that nobody has checked.
The other thing Bayesian methods do well is take experts seriously. If someone at your organisation has spent twenty years learning how the system behaves, that knowledge can go into the model as a prior rather than being overridden by whatever the last six months of data happened to show. It’s a way of combining human judgement with statistical machinery instead of choosing between them.
I’ve applied the same methods to black hole simulations, bacterial imaging, insurance claims, retail siting and language reconstruction. The terminology changes completely between those; the underlying problems barely change at all. Finding that translation is most of the job.