Central Limit Theorem

Let be a set of independent random variates and each have an arbitrary probability distribution with mean and a finite variance . Then the normal form variate

(1)

has a limiting cumulative distribution function which approaches a normal distribution.

Under additional conditions on the distribution of the addend, the probability density itself is also normal (Feller 1971) with mean and variance . If conversion to normal form is not performed, then the variate

(2)

is normally distributed with and .

Kallenberg (1997) gives a six-line proof of the central limit theorem. For an elementary, but slightly more cumbersome proof of the central limit theorem, consider the inverse Fourier transform of .

			(3)
			(4)
			(5)
			(6)

Now write

(7)

so we have

			(8)
			(9)
			(10)
			(11)
			(12)
			(13)
			(14)
			(15)
			(16)

Now expand

(17)

so

			(18)
			(19)
			(20)

since

			(21)
			(22)

Taking the Fourier transform,

			(23)
			(24)

This is of the form

(25)

where and . But this is a Fourier transform of a Gaussian function, so

(26)

(e.g., Abramowitz and Stegun 1972, p. 302, equation 7.4.6). Therefore,

			(27)
			(28)
			(29)

But and , so

(30)

The "fuzzy" central limit theorem says that data which are influenced by many small and unrelated random effects are approximately normally distributed.

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