central limit theorem
E4991
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.
All labels observed (8)
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
probability theorem
ⓘ
statistical theorem ⓘ |
| alsoKnownAs | CLT ⓘ |
| appliesTo |
identically distributed random variables
ⓘ
independent random variables ⓘ |
| approximationQuality | improves as sample size increases ⓘ |
| conclusionDistribution | normal distribution ⓘ |
| describes | convergence in distribution of normalized sums of random variables ⓘ |
| doesNotRequire | original distribution to be normal ⓘ |
| failsIf | variance is infinite ⓘ |
| field |
probability theory
ⓘ
statistics ⓘ |
| hasApplicationDomain |
econometrics
ⓘ
engineering ⓘ machine learning ⓘ physics ⓘ quantitative finance ⓘ |
| hasFormulation |
convergence of sample mean to normal distribution
ⓘ
convergence of standardized sum to standard normal distribution ⓘ |
| hasHistoricalContributor |
Abraham de Moivre
ⓘ
Aleksandr Lyapunov ⓘ Carl Friedrich Gauss ⓘ Jarl Waldemar Lindeberg ⓘ Pierre-Simon Laplace ⓘ |
| hasVariant |
central limit theorem
self-linksurface differs
ⓘ
surface form:
Lindeberg central limit theorem
central limit theorem self-linksurface differs ⓘ
surface form:
Lyapunov central limit theorem
central limit theorem self-linksurface differs ⓘ
surface form:
central limit theorem for martingales
central limit theorem for triangular arrays ⓘ classical central limit theorem ⓘ functional central limit theorem ⓘ central limit theorem self-linksurface differs ⓘ
surface form:
multivariate central limit theorem
|
| implies | approximate normality of sample mean for large samples ⓘ |
| isTypeOf |
convergence theorem
ⓘ
limit theorem ⓘ |
| relatedTo |
Berry–Esseen theorem
ⓘ
characteristic functions ⓘ law of large numbers ⓘ moment generating functions ⓘ stable distributions ⓘ |
| requiresCondition |
finite mean
ⓘ
finite variance ⓘ |
| statesThat | the standardized sum of many independent identically distributed random variables converges in distribution to a normal distribution ⓘ |
| underpins |
asymptotic normality of estimators
ⓘ
large-sample theory in statistics ⓘ |
| usedFor |
approximating sampling distributions
ⓘ
construction of confidence intervals ⓘ error analysis in Monte Carlo methods ⓘ hypothesis testing ⓘ normal approximation to Poisson distribution ⓘ normal approximation to binomial distribution ⓘ |
Referenced by (20)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Lindeberg central limit theorem
this entity surface form:
Lyapunov central limit theorem
this entity surface form:
multivariate central limit theorem
this entity surface form:
central limit theorem for martingales
this entity surface form:
Lindeberg–Feller central limit theorem
this entity surface form:
Lyapunov central limit theorem
this entity surface form:
central limit theorem for martingales
this entity surface form:
Lyapunov central limit theorem
subject surface form:
Probability theory
this entity surface form:
classical central limit theorem