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