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Only in certain circumstances:

The probability of success, p, in each trial must be close to 0.

Then, for the random variable, X = number of successes in n trials, the mean is np

and the variance is np(1-p). But since p is close to 0, (1-p) is close to 1 and so np(1-p) is close to np.

That is, the mean of the distribution is close to its variance. This is a characteristic of the Poisson distribution.

Furthermore, the other characteristics of the distribution: constant probability, independence are met so the Binomial can be approximated by the Poisson.

It is possible to prove this analytically but the limitations of this browser - especially in terms of mathematical notation - preclude that.

Only in certain circumstances:

The probability of success, p, in each trial must be close to 0.

Then, for the random variable, X = number of successes in n trials, the mean is np

and the variance is np(1-p). But since p is close to 0, (1-p) is close to 1 and so np(1-p) is close to np.

That is, the mean of the distribution is close to its variance. This is a characteristic of the Poisson distribution.

Furthermore, the other characteristics of the distribution: constant probability, independence are met so the Binomial can be approximated by the Poisson.

It is possible to prove this analytically but the limitations of this browser - especially in terms of mathematical notation - preclude that.

Only in certain circumstances:

The probability of success, p, in each trial must be close to 0.

Then, for the random variable, X = number of successes in n trials, the mean is np

and the variance is np(1-p). But since p is close to 0, (1-p) is close to 1 and so np(1-p) is close to np.

That is, the mean of the distribution is close to its variance. This is a characteristic of the Poisson distribution.

Furthermore, the other characteristics of the distribution: constant probability, independence are met so the Binomial can be approximated by the Poisson.

It is possible to prove this analytically but the limitations of this browser - especially in terms of mathematical notation - preclude that.

Only in certain circumstances:

The probability of success, p, in each trial must be close to 0.

Then, for the random variable, X = number of successes in n trials, the mean is np

and the variance is np(1-p). But since p is close to 0, (1-p) is close to 1 and so np(1-p) is close to np.

That is, the mean of the distribution is close to its variance. This is a characteristic of the Poisson distribution.

Furthermore, the other characteristics of the distribution: constant probability, independence are met so the Binomial can be approximated by the Poisson.

It is possible to prove this analytically but the limitations of this browser - especially in terms of mathematical notation - preclude that.

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11y ago

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Only in certain circumstances:

The probability of success, p, in each trial must be close to 0.

Then, for the random variable, X = number of successes in n trials, the mean is np

and the variance is np(1-p). But since p is close to 0, (1-p) is close to 1 and so np(1-p) is close to np.

That is, the mean of the distribution is close to its variance. This is a characteristic of the Poisson distribution.

Furthermore, the other characteristics of the distribution: constant probability, independence are met so the Binomial can be approximated by the Poisson.

It is possible to prove this analytically but the limitations of this browser - especially in terms of mathematical notation - preclude that.

User Avatar

Wiki User

11y ago
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Q: Why binomial distribution can be approximated by Poisson distribution?
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