Reading the evidence
Probability represents uncertainty on a scale from impossible to certain under a defined model or evidence base. It describes possible outcomes, not a hidden guarantee about one event.
Make it click
Several doors may open, with some more likely than others. Assigning weights describes uncertainty before the door opens; afterwards one outcome occurs without proving the weights were perfect.
Now put that into markets
Market probabilities may come from frequencies, models, prices or judgment. Each source carries assumptions, and probabilities should update when information changes.
Mutually exclusive exhaustive outcomes sum to one. Conditional probability changes the reference set after new information is known.
Define event, horizon and conditioning information. Test calibration across many comparable forecasts rather than judging one realised event.
A 70% event can fail without the forecast being impossible, and vague events cannot have auditable probabilities. Model probability is not objective certainty.
Takeaway
