think-natural-frequency-bayesianlisted

<!-- thinking-framework-skills | https://github.com/product-on-purpose/thinking-framework-skills | Apache-2.0 --> # Natural-Frequency Bayesian Framing People - including experts - reason badly about conditional probabilities stated as percentages, because they neglect the base rate. Re-expressing the same facts as natural frequencies over a concrete population makes the correct answer nearly visible: "Out of 1,000, 10 have it; 9 of those test positive; of the 990 without it, ~89 also test positive; so of ~98 positives, only 9 truly have it - about 9%." The format does the work by keeping the base rate in the counts. The output is a **natural-frequency breakdown**. Honest constraint: the base rate and hit rates must be real - the format makes correct reasoning tractable, it does not invent the inputs. ## When to Use - Interpreting a test or screening result (medical, fraud, security, lead-scoring, A/B). - Any "given a positive signal, what is the actual probability the thing is true?" question. - Communicating risk to others so they do not over-read a positive. ## When NOT to Use - When you do not have real input rates and would have to invent them. - When there is no conditional-probability structure to the question. - For general project forecasting (use reference-class forecasting). - When a single point estimate is wanted and the base-rate structure is irrelevant. ## Instructions When asked to reason about a conditional probability, follow these steps: 1. **State t