Central limit theorem: Difference between revisions
From ACT Wiki
Jump to navigationJump to search
imported>Administrator (CSV import) |
imported>Doug Williamson m (Link with qualifications page.) |
||
| Line 8: | Line 8: | ||
== See also == | == See also == | ||
* [[CertFMM]] | |||
* [[Sample]] | * [[Sample]] | ||
* [[Sampling]] | * [[Sampling]] | ||
Revision as of 15:50, 1 November 2014
It states formally that the average of a large number of independent identically distributed random variables will have a normal distribution.
The central limit theorem is important in sampling theory. It explains that sample means follow a normal distribution - regardless of the actual distribution of the parent population - and that the sample mean is an unbiased estimate of the parent population mean.
The central limit theorem also explains why larger samples will - on average - produce better estimates of the parent population mean.
The central limit theorem is sometimes known as the law of large numbers.
