|
gptkbp:instanceOf
|
statistical theorem
|
|
gptkbp:applies_when
|
sample size is sufficiently large
|
|
gptkbp:appliesTo
|
identically distributed variables
independent random variables
|
|
gptkbp:category
|
probability theorems
statistical theorems
|
|
gptkbp:compatibleWith
|
normality of original distribution
|
|
gptkbp:describes
|
distribution of sample means
|
|
gptkbp:field
|
gptkb:probability_theory
statistics
|
|
gptkbp:first_formulated_by
|
gptkb:Abraham_de_Moivre
|
|
gptkbp:further_developed_by
|
gptkb:Carl_Friedrich_Gauss
gptkb:Pierre-Simon_Laplace
gptkb:Aleksandr_Lyapunov
|
|
gptkbp:generalizes
|
gptkb:Lindeberg–Feller_theorem
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Central limit theorem
|
|
gptkbp:implies
|
sample mean approximates normal distribution as sample size increases
|
|
gptkbp:namedAfter
|
gptkb:normal_distribution
|
|
gptkbp:relatedTo
|
gptkb:Berry–Esseen_theorem
gptkb:law_of_large_numbers
|
|
gptkbp:requires
|
finite variance
|
|
gptkbp:state
|
sum of a large number of independent random variables tends toward a normal distribution
|
|
gptkbp:used_in
|
hypothesis testing
inferential statistics
confidence intervals
|
|
gptkbp:bfsParent
|
gptkb:Crash_Course_Statistics
gptkb:Standard_normal_distribution
|
|
gptkbp:bfsLayer
|
7
|