Fisher's exact test
E212220
Fisher's exact test is a statistical significance test used to determine whether there are nonrandom associations between two categorical variables in a contingency table, especially with small sample sizes.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Fisher's exact test canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1908299 — 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: Fisher's exact test Context triple: [Ronald A. Fisher, knownFor, Fisher's exact test]
-
A.
Cochran Square
Cochran Square is a public square located in the coastal city of Brunswick, Georgia.
-
B.
Hotelling’s T-squared distribution
Hotelling’s T-squared distribution is a multivariate generalization of Student’s t-distribution used primarily for hypothesis testing and constructing confidence regions for mean vectors in multivariate statistics.
-
C.
Frisch–Waugh–Lovell theorem
The Frisch–Waugh–Lovell theorem is a fundamental result in econometrics that shows how the coefficients of a multiple linear regression can be obtained by first partialling out (regressing out) other explanatory variables.
-
D.
Bhattacharyya distance
Bhattacharyya distance is a statistical measure of similarity between two probability distributions, often used in pattern recognition and classification to quantify their overlap.
-
E.
Bayes’ theorem
Bayes’ theorem is a fundamental result in probability theory that describes how to update the probability of a hypothesis based on new evidence.
- 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: Fisher's exact test Target entity description: Fisher's exact test is a statistical significance test used to determine whether there are nonrandom associations between two categorical variables in a contingency table, especially with small sample sizes.
-
A.
Cochran Square
Cochran Square is a public square located in the coastal city of Brunswick, Georgia.
-
B.
Hotelling’s T-squared distribution
Hotelling’s T-squared distribution is a multivariate generalization of Student’s t-distribution used primarily for hypothesis testing and constructing confidence regions for mean vectors in multivariate statistics.
-
C.
Frisch–Waugh–Lovell theorem
The Frisch–Waugh–Lovell theorem is a fundamental result in econometrics that shows how the coefficients of a multiple linear regression can be obtained by first partialling out (regressing out) other explanatory variables.
-
D.
Bhattacharyya distance
Bhattacharyya distance is a statistical measure of similarity between two probability distributions, often used in pattern recognition and classification to quantify their overlap.
-
E.
Bayes’ theorem
Bayes’ theorem is a fundamental result in probability theory that describes how to update the probability of a hypothesis based on new evidence.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
exact test
ⓘ
nonparametric test ⓘ statistical hypothesis test ⓘ test of independence ⓘ |
| alternativeTo |
Pearson's chi-squared test
ⓘ
linked to:
chi-square distribution
|
| appliesTo |
binary variables
ⓘ
categorical data ⓘ nominal variables ⓘ |
| assumes |
fixed margins in the contingency table
ⓘ
hypergeometric sampling model ⓘ |
| basedOn | hypergeometric distribution ⓘ |
| canBe |
one-sided
ⓘ
two-sided ⓘ |
| canBeGeneralizedTo | r x c contingency tables ⓘ |
| category |
exact statistical methods
ⓘ
statistical tests ⓘ |
| compares | observed cell counts to all possible tables with same margins ⓘ |
| computes | exact p-value ⓘ |
| domain |
biostatistics
ⓘ
epidemiology ⓘ social sciences ⓘ statistics ⓘ |
| historicalNote | introduced by Ronald A. Fisher in the 20th century ⓘ |
| implementedIn |
Python statistical libraries
ⓘ
R ⓘ SAS ⓘ SPSS ⓘ
linked to:
IBM SPSS Statistics
Stata ⓘ |
| namedAfter | Ronald A. Fisher ⓘ |
| output | p-value for association ⓘ |
| preferredWhen |
expected cell counts are less than 5
ⓘ
sample size is small ⓘ |
| relatedConcept |
contingency table
ⓘ
exact inference ⓘ odds ratio ⓘ |
| relatedTo | chi-squared test of independence ⓘ |
| typicalInput | 2x2 contingency table ⓘ |
| usedFor |
analyzing 2x2 contingency tables
ⓘ
exact inference on odds ratio ⓘ situations with low expected cell counts ⓘ small sample size data analysis ⓘ testing association between two categorical variables ⓘ testing independence in contingency tables ⓘ |
| usedIn |
case-control studies
ⓘ
clinical trials ⓘ contingency table analysis software ⓘ genetics association studies ⓘ |
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: Fisher's exact test Description of subject: Fisher's exact test is a statistical significance test used to determine whether there are nonrandom associations between two categorical variables in a contingency table, especially with small sample sizes.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.