DEFINITION of 'Central Limit Theorem  CLT'
A statistical theory that states that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population. Furthermore, all of the samples will follow an approximate normal distribution pattern, with all variances being approximately equal to the variance of the population divided by each sample's size.
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BREAKING DOWN 'Central Limit Theorem  CLT'
This statistical theory is very useful when examining returns for a given stock or index because it simplifies many analysis procedures. An appropriate sample size depends on the data available, but generally speaking, having a sample size of at least 50 observations is sufficient. Due to the relative ease of generating financial data, it is often easy to produce much larger sample sizes.

Variance
The spread between numbers in a data set, measuring Variance ... 
Normal Distribution
A probability distribution that plots all of its values in a ... 
Sampling Error
A statistical error to which an analyst exposes a model simply ... 
Probability Distribution
A statistical function that describes all the possible values ... 
NonSampling Error
A statistical error caused by human error to which a specific ... 
Binomial Distribution
A probability distribution that summarizes the likelihood that ...

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Descriptive statistics is the term applied to meaningful data analysis.

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