Central limit theorem: Difference between revisions
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imported>Doug Williamson (Identify context as financial maths & link with Normal distribution page.) |
imported>Doug Williamson (Classify page.) |
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The central limit theorem states formally that the average of a large number of independent identically distributed random variables will have a normal distribution. | The central limit theorem 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 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 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''. | The central limit theorem is sometimes known as the '' law of large numbers''. | ||
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== See also == | == See also == | ||
* [[Mean]] | * [[Mean]] | ||
* [[Normal distribution]] | * [[Normal frequency distribution]] | ||
* [[Sample]] | * [[Sample]] | ||
* [[Sampling]] | * [[Sampling]] | ||
[[Category:The_business_context]] | |||
[[Category:Identify_and_assess_risks]] | [[Category:Identify_and_assess_risks]] | ||
Latest revision as of 20:54, 1 July 2022
Financial maths.
The central limit theorem 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.
