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The importance is that the sum of a large number of independent random variables is always approximately normally distributed as long as each random variable has the same distribution and that distribution has a finite mean and variance. The point is that it DOES NOT matter what the particular distribution is. So whatever distribution you start with, you always end up with normal.

Q: What is importance of central limit theorem?

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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.

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

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.

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

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The Central Limit THeorem say that the sampling distribution of .. is ... It would help if you read your question before posting it.

The Central Limit Theorem (abbreviated as CLT) states that random variables that are independent of each other will have a normally distributed mean.

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.

You use the central limit theorem when you are performing statistical calculations and are assuming the data is normally distributed. In many cases, this assumption can be made provided the sample size is large enough.

This is the Central Limit Theorem.

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.

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the central limit theorem

Central Limit Theorem

The central limit theorem is one of two fundamental theories of probability. It's very important because its the reason a great number of statistical procedures work. The theorem states the distribution of an average has the tendency to be normal, even when it turns out that the distribution from which the average is calculated is definitely non-normal.

It is a result of the Central Limit Theorem.

Because other than in a degenerate case, the maximum of a set of observations is not at its centre! And the theorem concerns the distribution of estimates of the central value - as the name might suggest!