What does it mean that bayesian updating in practice?

Update the strength of your beliefs proportionally to the strength of new evidence.

Example: You believe there is a 70% chance your team will ship the product on time. Then the lead engineer quits. A naive thinker either ignores this (conservatism) or panics and drops to 5% (overreaction). A Bayesian updater asks: how likely is a lead engineer quitting if the project were on track versus off track? The answer shifts the estimate — maybe to 40% — proportional to the diagnostic value of the evidence, not to its emotional intensity. The number is not the point. The discipline of proportional adjustment is the point.

Try this: Pick a belief you currently hold with moderate confidence — a prediction about your career, a judgment about a colleague's competence, an assumption about how a project will unfold. Write it down with a probability: 'I am X% confident that Y.' Now identify the single most important piece of evidence that could change this belief. What would you expect to see if your belief were correct? What would you expect to see if it were wrong? Assign your best estimate of how much that evidence should move your number, and in which direction. Check back in one week. Did the evidence arrive? Did you actually update? By how much? Compare what you planned to do with what you actually did. The gap between planned and actual updating is your conservatism signature.