CLT
E31545
CLT is a fundamental statistical principle stating that the sum or average of many independent, identically distributed random variables tends to follow a normal distribution, regardless of the original distribution.
All labels observed (1)
| Label | Occurrences |
|---|---|
| CLT canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T243777 — 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: CLT Context triple: [central limit theorem, alsoKnownAs, CLT]
-
A.
CLIC
CLIC is a proposed high-energy electron–positron linear collider at CERN designed to explore physics beyond the Standard Model with multi-TeV collisions.
-
B.
CIT
CIT is the commonly used acronym for the Center for Information Technology, an organization focused on advancing computing and information systems.
-
C.
LCC
LCC is a comprehensive library classification system developed by the Library of Congress to organize and arrange books and other materials by subject.
-
D.
CLP
CLP is the three-letter ISO 4217 currency code for the Chilean peso, the official monetary unit of Chile.
-
E.
CUL
CUL is the main research library of the University of Cambridge and one of the largest and most important academic libraries in the United Kingdom.
- 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: CLT Target entity description: CLT is a fundamental statistical principle stating that the sum or average of many independent, identically distributed random variables tends to follow a normal distribution, regardless of the original distribution.
-
A.
CLIC
CLIC is a proposed high-energy electron–positron linear collider at CERN designed to explore physics beyond the Standard Model with multi-TeV collisions.
-
B.
CIT
CIT is the commonly used acronym for the Center for Information Technology, an organization focused on advancing computing and information systems.
-
C.
LCC
LCC is a comprehensive library classification system developed by the Library of Congress to organize and arrange books and other materials by subject.
-
D.
CLP
CLP is the three-letter ISO 4217 currency code for the Chilean peso, the official monetary unit of Chile.
-
E.
CUL
CUL is the main research library of the University of Cambridge and one of the largest and most important academic libraries in the United Kingdom.
- F. None of above. chosen
Statements (43)
| Predicate | Object |
|---|---|
| instanceOf |
probability theory concept
ⓘ
statistical theorem ⓘ |
| appliesTo |
identically distributed random variables
ⓘ
independent random variables ⓘ sample means ⓘ sums of random variables ⓘ |
| approximationImprovesWith | increasing sample size ⓘ |
| assumes | no single variable dominates the sum ⓘ |
| category |
asymptotic result in statistics
ⓘ
limit theorem ⓘ |
| describes |
approximate normality of sample means
ⓘ
convergence in distribution of normalized sums of random variables ⓘ |
| enables | approximate normal-based methods for non-normal populations ⓘ |
| field |
probability theory
ⓘ
statistics ⓘ |
| formalizes | emergence of normality from aggregation of random effects ⓘ |
| fullName |
central limit theorem
ⓘ
surface form:
Central Limit Theorem
|
| hasVariant |
central limit theorem
ⓘ
surface form:
Lindeberg–Feller central limit theorem
central limit theorem ⓘ
surface form:
Lyapunov central limit theorem
central limit theorem ⓘ
surface form:
central limit theorem for martingales
multivariate central limit theorem ⓘ |
| historicallyAssociatedWith |
Abraham de Moivre
ⓘ
Aleksandr Lyapunov ⓘ Carl Friedrich Gauss ⓘ Pierre-Simon Laplace ⓘ |
| holdsUnder |
appropriate moment conditions
ⓘ
independence or weak dependence conditions ⓘ |
| implies |
distribution of standardized sums tends to normal distribution
ⓘ
sample mean is approximately normally distributed for large samples ⓘ |
| mathematicalForm | normalized sum converges in distribution to N(0,1) ⓘ |
| relatedTo |
Gaussian distribution
ⓘ
law of large numbers ⓘ normal distribution ⓘ standardization of random variables ⓘ |
| requires |
finite mean
ⓘ
finite variance ⓘ |
| supports | use of z-scores in large-sample inference ⓘ |
| typicalSampleSizeRuleOfThumb | n ≥ 30 for many practical applications ⓘ |
| usedFor |
construction of confidence intervals
ⓘ
error analysis in sampling ⓘ hypothesis testing ⓘ normal approximations to discrete distributions ⓘ statistical inference ⓘ |
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: CLT Description of subject: CLT is a fundamental statistical principle stating that the sum or average of many independent, identically distributed random variables tends to follow a normal distribution, regardless of the original distribution.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.