Central limit theorem: 7

7.1.2 Central Limit Theorem The central limit theorem (CLT) is one of the most important results in probability theory. It states that, under certain conditions, the sum of a large number of random variables is approximately normal. Here, we state a version of the CLT that applies to i.i.d. random variables. The Central Limit Theorem (CLT ) relies on multiple independent samples that are randomly selected to predict the activity of a population. Learn how the sampling distribution of the mean approaches a normal distribution as the sample size increases, with examples of uniform and binomial distributions. See plots, formulas, and explanations of the central limit theorem . The Central Limit Theorem defines that the mean of all the given samples of a population is the same as the mean of the population (approx) if the sample size is sufficiently large enough with a finite variation. It is one of the main topics of statistics. Also, learn: Statistics Population and Sample Sampling Methods In this article, let us discuss the “ Central Limit Theorem ” with the help of an example to understand this concept better. Central Limit Theorem Definition The Central ...