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As we know, each random variable is responsible for associating some random events to the probability values. These random events belong to the specific population, and that random variable represent that population. In other words, random variables are representatives of their populations. By virtue of this, they present their own distributions.

What I am wondering is that summing adequate number of random variables

According to the resource that I read two days ago, by summing large number of random variables, we can obtain new random variable of normal distribution. Besides, this is called central limit theorem.

Actually, I investigated central limit theorem, and I could not construct a relationship between the theorem itself and summing random variables. The theorem is about the fact that large amount of data samples construct a normal distribution.

Can anyone explain if there is a relationship between summing random variables between central limit theorem ?

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The classical central limit theorem states that, given a large sample of independent values $X_n$ from the same finite-$\mu$-and$\sigma$ distribution, $\frac{1}{\sqrt{n}}\sum_{i=1}^n\frac{X_i-\mu}{\sigma}\approx N(0,\,1)$. This approximation is equivalent to the sample mean being $N(\mu,\,\frac{\sigma^2}{n})$, but the former statement is preferred so the distribution converges.

Note the Normal approximation we obtain is of the sample mean (give or take your preferred linear transformation thereof for the discussion), not of the distribution being sampled. One common misconception is that Normal distributions themselves are supposed on this theorem to be prevalent "in the real world".

Note also that if the sampled distribution doesn't have a finite mean and variance, this all falls apart. And in the famous example of a Cauchy distribution, the sample mean actually still has the same Cauchy distribution. (The sample median, on the other hand, is approximately Normal.)

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    $\begingroup$ Excellent point re misconceptions of normal dists in the real world. This paper is a fascinating exploration of such misconceptions: aidanlyon.com/normal_distributions.pdf $\endgroup$ Commented May 27, 2020 at 18:37
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    $\begingroup$ @ColmBhandal That paper's a classic. I also recommend Chapter 7 of Probability Theory: The Logic of Science , which explains why empirical errors tend to be Normal. $\endgroup$
    – J.G.
    Commented May 27, 2020 at 18:41
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Say you have a probability distribution function (PDF) which has a finite mean $\mu$ and variance $\sigma$. This PDF does not have to be a well-known PDF like Poisson or Gaussian; it can have an uncommon profile.

1) If you generate from this PDF large enough number of random numbers, the histogram of those random numbers will reveal this PDF itself. Of course, this is trivially true. The Central Limit Theorem is not relevant here.

2) Now imagine you generate, say, 30 different sets (samples) from this PDF, each set containing, say, 50 random numbers. Now calculate the mean of each set to get 30 means. Now plot the histogram of those means (not the random numbers), and you will see that the histogram of those means will be a Gaussian regardless of the PDF that you used to generate the random numbers. This is what the Central Limit Theorem tells us.

Note that 1) is about the random numbers themselves generated from the PDF while 2) is about the distribution of the "means", not the random numbers themselves. The common misconception is that if you sample large enough numbers from a PDF, you'll get Gaussian regardless of the underlying PDF. Of course this is trivially false.

Hope this helps.

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