Central Limit Theorem Illustrated with Four Probability Distributions

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We consider four different probability distributions for as a function of between the interval and ; outside this interval, the probability is set to 0. The are , , and one that is discrete; is the constant that enables the integral of the probability distribution to be normalized; that is, .

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Use the "trials T" slider to set the number of samples, each one of size , sampled from the corresponding .

The top plot shows the plot of the selected ; a red line indicates the position of the probability distribution . On the right, the values of and of are indicated.

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Contributed by: D. Meliga and S. Z. Lavagnino (May 2018)
Additional contribution by: G. Valorio
Open content licensed under CC BY-NC-SA


Snapshots


Details

Snapshot 1: a small and small result in a poor approximation of a Gaussian; this is also shown by the discrepancy between estimated and real values

Snapshot 2: a large and small result in a poor approximation of a Gaussian, but an accordance between estimated and real values can be found

Snapshot 3: a small and large result in a good approximation of a Gaussian, and the estimated and real values are close to each other

Snapshot 4: a large and a large result in a very good approximation of a Gaussian, and the estimated and real values are close to each other

Reference

[1] R. E. Walpole, R. H. Myers, S. L. Myers and K. Ye, Probability & Statistics for Engineers & Scientists, 9th ed., Boston: Prentice Hall, 2012.



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