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 |