Central Limit Theorem - Mathematical Methods of Physics, UGC - NET Physics | Physics for IIT JAM, UGC - NET, CSIR NET PDF Download

We don't have the tools yet to prove the Central Limit Theorem, so we'll just go ahead and state it without proof. 

Central Limit Theorem. Let X₁, X₂, ... , X_n be a random sample from a distribution (any distribution!) with (finite) mean μ and (finite) variance σ². If the sample size n is "sufficiently large," then:

(1) the sample mean  follows an approximate normal distribution

(2) with mean E

(3) and variance 

We write:

or:

So, in a nutshell, the Central Limit Theorem (CLT) tells us that the sampling distribution of the sample mean is, at least approximately, normally distributed, regardless of the distribution of the underlying random sample. In fact, the CLT applies regardless of whether the distribution of the X_i is discrete (for example, Poisson or binomial) or continuous (for example, exponential or chi-square). Our focus in this lesson will be on continuous random variables. In the next lesson, we'll apply the CLT to discrete random variables, such as the binomial and Poisson random variables.

You might be wondering why "sufficiently large" appears in quotes in the theorem. Well, that's because the necessary sample size n depends on the skewness of the distribution from which the random sample X_i comes:

1. If the distribution of the X_i is symmetric, unimodal or continuous, then a sample size n as small as 4 or 5 yields an adequate approximation.

2. If the distribution of the X_i is skewed, then a sample size n of at least 25 or 30 yields an adequate approximation.

3. If the distribution of the X_i is extremely skewed, then you may need an even larger n.

We'll spend the rest of the lesson trying to get an intuitive feel for the theorem, as well as applying the theorem so that we can calculate probabilities concerning the sample mean.