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The central limit theorem: the arithmetic mean of a sufficient number of independent random variables with means and variances will be a normal random variable. It's just that lots of natural phenomena are like this, or appear to be sufficiently like this. Please see the wikipedia article for more details.

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Related Questions

Is the standard normal curve non symmetrical?

The standard normal curve is symmetrical.


Normal curve is the meaning of standard normal distribution?

No, the normal curve is not the meaning of the Normal distribution: it is one way of representing it.


What are the properties of a standard normal curve?

It is a normal curve with mean = 0 and variance = 1.


What is the area under the standard normal curve?

the standard normal curve 2


He area under the standard normal curve is?

The area under the standard normal curve is 1.


What makes ormal curve asymmetrical?

There is no such thing as an "ormal curve". And a Normal curve IS symmetrical!


The area under the normal curve is greatest in which scenario?

The area under the normal curve is ALWAYS 1.


Can you compare and contrast the skewness and normal curve?

Answer this question...similarities and differences between normal curve and skewness


Why is the normal curve significant?

Your question makes no sense. Significant is a word related to tests. The normal curve is a distribution, not a test.


What is the mean of standard normal curve?

The mean of a standard normal curve is 0. This curve, which is a type of probability distribution known as the standard normal distribution, is symmetric and bell-shaped, centered around the mean. Additionally, the standard deviation of a standard normal curve is 1, which helps define the spread of the data around the mean.


Why if a probability distribution curve is bell shaped why is this a normal distribution?

A bell shaped probability distribution curve is NOT necessarily a normal distribution.


What is gaussian frequency curve?

The Gaussian curve is the Normal distributoin curve, the commonest (and most studied) of statistical distributions.

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