Calculated Risks
E890551
Calculated Risks is a popular science book by psychologist Gerd Gigerenzer that explains how to better understand and communicate statistical information and probabilities in everyday decision-making.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Calculated Risks canonical | 3 |
| Calculated Risks: How to Know When Numbers Deceive You | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T10884978 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Calculated Risks Context triple: [Gerd Gigerenzer, notableWork, Calculated Risks]
-
A.
Not Without Risk
"Not Without Risk" is an album by American percussionist and shamanic practitioner Byron Metcalf, known for its deep, trance-inducing ambient and tribal rhythms.
-
B.
A Cautionary Tale
"A Cautionary Tale" is an opening number from the Mean Girls stage musical that sets up the story’s themes of high school hierarchy, bullying, and the consequences of social cruelty.
-
C.
The New Danger
The New Danger is a 2004 studio album by American rapper and actor Mos Def that blends hip hop with rock, soul, and blues influences.
-
D.
The Taming of Chance
The Taming of Chance is a influential philosophical and historical study by Ian Hacking that examines how concepts of probability and statistical thinking transformed modern understandings of chance, causality, and social regulation.
-
E.
Mortal Causes
Mortal Causes is a crime novel by Scottish author Ian Rankin, featuring Inspector Rebus investigating a politically charged murder in Edinburgh.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Calculated Risks Target entity description: Calculated Risks is a popular science book by psychologist Gerd Gigerenzer that explains how to better understand and communicate statistical information and probabilities in everyday decision-making.
-
A.
Not Without Risk
"Not Without Risk" is an album by American percussionist and shamanic practitioner Byron Metcalf, known for its deep, trance-inducing ambient and tribal rhythms.
-
B.
A Cautionary Tale
"A Cautionary Tale" is an opening number from the Mean Girls stage musical that sets up the story’s themes of high school hierarchy, bullying, and the consequences of social cruelty.
-
C.
The New Danger
The New Danger is a 2004 studio album by American rapper and actor Mos Def that blends hip hop with rock, soul, and blues influences.
-
D.
The Taming of Chance
The Taming of Chance is a influential philosophical and historical study by Ian Hacking that examines how concepts of probability and statistical thinking transformed modern understandings of chance, causality, and social regulation.
-
E.
Mortal Causes
Mortal Causes is a crime novel by Scottish author Ian Rankin, featuring Inspector Rebus investigating a politically charged murder in Edinburgh.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf | book ⓘ |
| aimsTo |
improve communication of risk
ⓘ
improve understanding of statistical information ⓘ |
| author | Gerd Gigerenzer ⓘ |
| basedOn | research on heuristics and biases ⓘ |
| countryOfOrigin |
United States
ⓘ
linked to:
United States of America
|
| criticizes |
misleading statistical communication
ⓘ
use of relative risk without base rates ⓘ |
| explainsConcept |
Bayes' theorem
ⓘ
linked to:
Bayes’ theorem
absolute risk ⓘ base rate ⓘ conditional probability ⓘ false negative rate ⓘ false positive rate ⓘ natural frequencies ⓘ relative risk ⓘ risk communication ⓘ statistical illiteracy ⓘ |
| focusesOn |
everyday decisions
ⓘ
legal decision-making ⓘ medical decision-making ⓘ |
| genre |
non-fiction
ⓘ
popular science ⓘ |
| hasPerspective | psychological approach to statistics ⓘ |
| hasSubject | Gerd Gigerenzer ⓘ |
| hasSubtitle | How to Know When Numbers Deceive You ⓘ |
| intendedAudience |
general public
ⓘ
health professionals ⓘ lawyers ⓘ |
| languageStyle | accessible ⓘ |
| mainSubject |
decision-making
ⓘ
probability ⓘ risk literacy ⓘ statistics ⓘ |
| mediaType | print ⓘ |
| notableFor |
examples from medicine and law
ⓘ
popularizing natural frequency formats ⓘ |
| originalLanguage | English ⓘ |
| proposesMethod | using natural frequencies to present probabilities ⓘ |
| publicationYear | 2002 ⓘ |
| publisher | Simon & Schuster ⓘ |
| relatedWork |
Reckoning with Risk
ⓘ
Risk Savvy ⓘ |
| teachesSkill |
interpreting medical test results
ⓘ
interpreting probabilities ⓘ risk literacy ⓘ |
| workExampleOf | science communication ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Calculated Risks Description of subject: Calculated Risks is a popular science book by psychologist Gerd Gigerenzer that explains how to better understand and communicate statistical information and probabilities in everyday decision-making.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.