This is a Lexicon entry: the mechanism, when it shows up, and the countermeasure. The full correction, with real-world cases and the audit prompt, hasn't been written yet.
Definition
The foundational formula of probabilistic reasoning: the probability of a hypothesis given evidence equals the prior probability of the hypothesis times the likelihood of the evidence given the hypothesis, divided by the overall probability of the evidence. In plain language: new evidence should change your belief but by how much depends on your prior probability and the strength of the evidence. A weak prior combined with strong evidence produces a large update; a strong prior combined with weak evidence produces a small one.
When it shows up
When new evidence arrives; when deciding how much to update your beliefs. In any expert field: when understanding your own calibration helps you communicate uncertainty to stakeholders. In research: when hypothesis pre-registration forces you to articulate your prior before seeing data.
Failure mode
Either under-updating (not moving enough when evidence is strong) or over-updating (moving too much when evidence is weak). Both produce poor calibration.
Countermeasure
Make your prior explicit before encountering evidence. Weight new evidence by its likelihood ratio. Ask: "Given what I already knew, how much should this move me?"
Related concepts
Also connected in the mapCalibration·The False Positive Paradox·The Monty Hall Problem·Prior Probability
Origin
Also revealed
Entry #571 of 631 in The Lexicon · see the full Lexicon