SPIN RESULT // FILE 00
Bayesian Updating
“Only 1% of transactions are fraudulent. A fairly accurate detector raises an alert. Is fraud almost certain?”
PANEL A // CORE
The essential idea
Bayesian reasoning combines a prior probability with how much more likely the evidence is under one hypothesis than its alternatives. Evidence updates what we knew; it does not erase the base rate or turn uncertainty into certainty.
PANEL B // SEQUENCE
How it works
- 01
Begin with a prior rate grounded in relevant information.
- 02
Compare how expected the evidence is if each hypothesis were true.
- 03
Update the probability and keep it open to new evidence.
PANEL C // CASE
Concrete case
With 1% fraud, 90% sensitivity, and a 5% false-alarm rate, an alert means roughly a 15% chance of fraud—not 90%.
PANEL D // LIMIT
It does not mean this
A prior need not be arbitrary, and the posterior still represents uncertainty.
RHO // FIELD NOTE
Take it with you
Even an accurate test may create many false alarms when the condition it seeks is rare.
FINAL PANEL // APPLY
Transfer test
Before answering: how confident are you?