law of large numbers
E141078
The law of large numbers is a fundamental theorem in probability theory stating that as the number of independent trials increases, the sample average converges to the expected value.
All labels observed (3)
| Label | Occurrences |
|---|---|
| law of large numbers canonical | 10 |
| weak law of large numbers | 2 |
| strong law of large numbers | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1233847 — 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.
Target entity: law of large numbers Context triple: [Jakob Bernoulli, notableConcept, law of large numbers]
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A.
central limit theorem
The central limit theorem is a fundamental result in probability theory stating that the sum (or average) of many independent, identically distributed random variables tends to follow a normal distribution, regardless of the original variables’ distribution, under mild conditions.
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B.
Berry–Esseen theorem
The Berry–Esseen theorem is a quantitative refinement of the central limit theorem that provides explicit bounds on the rate of convergence of normalized sums of independent random variables to the normal distribution.
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C.
Sutton's law
Sutton's law is a medical and diagnostic principle that advises focusing first on the most likely cause of a problem, echoing bank robber Willie Sutton’s apocryphal rationale for targeting banks.
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D.
Théorie analytique des probabilités
Théorie analytique des probabilités is Pierre-Simon Laplace’s foundational treatise that systematically developed probability theory and laid the groundwork for modern statistics.
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E.
Gaussian law of error
The Gaussian law of error is a fundamental statistical principle stating that measurement errors tend to follow a normal (bell-shaped) distribution, forming the basis of much of probability theory and statistical inference.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Target entity: law of large numbers Target entity description: The law of large numbers is a fundamental theorem in probability theory stating that as the number of independent trials increases, the sample average converges to the expected value.
-
A.
central limit theorem
The central limit theorem is a fundamental result in probability theory stating that the sum (or average) of many independent, identically distributed random variables tends to follow a normal distribution, regardless of the original variables’ distribution, under mild conditions.
-
B.
Berry–Esseen theorem
The Berry–Esseen theorem is a quantitative refinement of the central limit theorem that provides explicit bounds on the rate of convergence of normalized sums of independent random variables to the normal distribution.
-
C.
Sutton's law
Sutton's law is a medical and diagnostic principle that advises focusing first on the most likely cause of a problem, echoing bank robber Willie Sutton’s apocryphal rationale for targeting banks.
-
D.
Théorie analytique des probabilités
Théorie analytique des probabilités is Pierre-Simon Laplace’s foundational treatise that systematically developed probability theory and laid the groundwork for modern statistics.
-
E.
Gaussian law of error
The Gaussian law of error is a fundamental statistical principle stating that measurement errors tend to follow a normal (bell-shaped) distribution, forming the basis of much of probability theory and statistical inference.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
law of large numbers variant
ⓘ
law of large numbers variant ⓘ limit theorem ⓘ probability theorem ⓘ statistical law ⓘ |
| alsoKnownAs | LLN ⓘ |
| appliesTo | independent identically distributed random variables ⓘ |
| assumption |
finite variance in many standard forms
ⓘ
identical distribution of trials ⓘ independence of trials ⓘ |
| category | asymptotic result ⓘ |
| clarifies | long-run behavior does not predict short-term outcomes ⓘ |
| concerns |
convergence of sample mean
ⓘ
long-run relative frequencies ⓘ |
| convergenceType |
almost sure convergence
ⓘ
convergence in probability ⓘ |
| field |
probability theory
ⓘ
statistics ⓘ |
| focusesOn |
expected value
ⓘ
sample mean ⓘ |
| formalizedIn | measure-theoretic probability ⓘ |
| formalStatementInvolves |
limits of sample means
ⓘ
probability measures ⓘ |
| guarantees | stabilization of averages with large sample size ⓘ |
| hasForm |
strong law of large numbers
ⓘ
law of large numbers self-linksurface differs ⓘ
surface form:
weak law of large numbers
|
| historicalContributor |
Andrei Kolmogorov
ⓘ
surface form:
Andrey Kolmogorov
Jakob Bernoulli ⓘ
surface form:
Jacob Bernoulli
Émile Borel ⓘ |
| implies |
empirical mean approximates theoretical mean
ⓘ
relative frequency approximates probability ⓘ |
| isFoundationFor |
frequency-based probability interpretation
ⓘ
large-sample statistical theory ⓘ |
| mathematicalObject | sequence of random variables ⓘ |
| misconception | gambler's fallacy ⓘ |
| relatesTo |
central limit theorem
ⓘ
ergodic theorem ⓘ |
| requires |
existence of finite expected value
ⓘ
increasing number of trials ⓘ |
| states | sample average converges to expected value as number of trials increases ⓘ |
| typicalExample |
repeated coin tosses
ⓘ
repeated dice rolls ⓘ |
| usedIn |
Monte Carlo method
ⓘ
surface form:
Monte Carlo methods
actuarial science ⓘ frequentist interpretation of probability ⓘ quality control ⓘ risk theory ⓘ statistical estimation ⓘ |
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.
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.
Subject: law of large numbers Description of subject: The law of large numbers is a fundamental theorem in probability theory stating that as the number of independent trials increases, the sample average converges to the expected value.
Referenced by (13)
Full triples — surface form annotated when it differs from this entity's canonical label.