Central limit theorem: The Central Limit Theorem in Statistics

The Central Limit Theorem in Statistics states that as the sample size increases and its variance is finite , then the distribution of the sample mean approaches the normal distribution, irrespective of the shape of the population distribution. The Central Limit Theorem (CLT) proves that the averages of samples from any distribution themselves must be normally distributed. Consider IID random variables 1, 2 such that . . . Central limit theorem states that the sampling distribution of means will approximate a normal distribution for a large sample. Understand central limit theorem using solved examples. 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 ...