The central limit theorem basically states that as the sample size gets large enough, the sampling distribution becomes more normal regardless of the population distribution.
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The central limit theorem states that the mean of a sufficiently large number of iterates of independent random variables, each with well-defined mean and well-defined variance, will be approximately distributed. This is the definition in the probability theory.
According to the Central Limit Theorem, even if a variable has an underlying distribution which is not Normal, the means of random samples from the population will be normally distributed with the population mean as its mean.
Central Limit Theorem
There is abig difference between them..gamma is a distribution but central limit theorm is just like a method or technique u use to approximate gamma to another distriution which is normal....stupid
Yes, and more so for larger samples. (It follows from the Central Limit Theorem.)