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.


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
hasVariant Lindeberg–Feller central limit theorem
Lyapunov central limit theorem
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

Referenced by (2)
Subject (surface form when different) Predicate
central limit theorem
alsoKnownAs
CLST
successorOf

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