probability
Let an agent handle probability by definition, interpretation, calculation and communication of risk, avoiding common fallacies.
Research draft, second pass
A second pass drafted this model: the structure a model of this thing needs, and what is known about it in the world. The line under this one says how the second half was obtained - researched against sources, or recalled without web access, in which case nothing here was read anywhere and every claim is a lead to verify. Unreviewed either way.
written by Claude from model knowledge without web access - no source was read, every claim is a lead to verify
Researched by: Claude
Purpose and description
Let an agent handle probability by definition, interpretation, calculation and communication of risk, avoiding common fallacies.
A numerical measure between 0 and 1 of how likely an event is, formalised by Kolmogorov axioms and interpreted in frequentist, Bayesian and other ways; related notions include joint, conditional and error probabilities.
What it is for: Reasoning under uncertainty in science, engineering, medicine, finance and daily life.
It can be compute probabilities of events; apply conditional probability and Bayes theorem; communicate risk clearly; spot common fallacies.
Distinguishing features
Measure of likelihood
Axiomatic foundation
Several interpretations
Easily misunderstood
What it looks like
Not physical; numbers, fractions and percentages.
How it is recognised
Values from 0 to 1 or 0 to 100 percent
Notation P(A)
Odds are a related but different expression
Related models
is a kind of - category
is studied by - fields
is related to - expressions
is used in - application
In practice
Families and kinds
classical probability
frequentist probability
Bayesian probability
conditional and joint probability
geometric probability
Standards and regulation
ISO 3534 statistics vocabulary
Failure modes and hazards
Base rate neglect
Gambler fallacy
Confusing relative and absolute risk
Also called
Where this came from
wikidata · CC0 1.0
Also registered as vr.tr.probability-attribute
Drafted structure
Bundle to layer to finding to question, as the second pass will find it: 4 bundles · 8 layers · 8 findings · 16 questions.
Calculation Computing.
Rules compute probability.
Event
Probability of event.
Event
Event probability.
- What is the probability of this event? measurement
- Which assumptions are used? boundary
Conditional
Bayes.
Conditional
Conditional probability.
- What is the probability given this evidence? measurement
- Is the base rate included? boundary
Interpretation Meaning.
Interpretations differ.
Meaning
Frequentist or Bayesian.
Meaning
Interpretation.
- Is the probability a long-run frequency or a degree of belief? definition
- Does it matter here? boundary
Model
Assumptions.
Model
Model assumptions.
- Which model generates the probability? provenance
- How sensitive is it to assumptions? boundary
Communication Risk.
Clear communication matters.
Format
Natural frequencies.
Format
Risk format.
- Would natural frequencies such as 1 in 100 be clearer? action
- Is absolute risk given alongside relative risk? boundary
Fallacies
Errors.
Fallacies
Fallacies.
- Is a common fallacy such as the gambler fallacy involved? boundary
- How can it be corrected? action
Applications Uses.
Probability informs decisions.
Medical
Tests.
Medical
Medical tests.
- What is the probability of disease given a positive test, in general terms? measurement
- Should results be discussed with a clinician? action
Gambling
Odds.
Gambling
Gambling odds.
- What are the true odds compared with offered odds? measurement
- Where can someone get help with problem gambling? action
What the second pass must settle
- Should interpretations be separate entries?
- How should risk communication guidance be linked?
- How should odds be related?