Poisson & normal distributions as approximations for the binomial

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SUMMARY

The discussion clarifies the use of the Poisson and normal distributions as approximations for the binomial distribution under specific conditions. The Poisson distribution is optimal when the probability of success (p) is very small, while the normal distribution is preferred when p is approximately 0.5 or when both Np and N(1-p) are large. The nuances presented by Hoel in "Introduction to Mathematical Statistics" indicate that both distributions can be effectively utilized depending on the size of n and the value of p. Thus, practitioners can confidently apply these approximations in statistical computations involving binomial probabilities.

PREREQUISITES
  • Understanding of binomial distribution
  • Familiarity with Poisson distribution
  • Knowledge of normal distribution
  • Basic statistical concepts related to density functions
NEXT STEPS
  • Study the conditions for using Poisson distribution as an approximation for binomial distribution
  • Learn about the Central Limit Theorem and its relation to normal distribution
  • Explore the implications of large sample sizes on statistical approximations
  • Investigate practical applications of Poisson and normal approximations in real-world scenarios
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Statisticians, data analysts, and students studying probability theory who seek to understand the application of Poisson and normal distributions in approximating binomial probabilities.

Rasalhague
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These three quotes talk about the use of the Poisson and normal distributions as approximations for the binomial when n is large. The first two quotes here say Poisson is best when p small, and the normal otherwise. The third seems to change the story; it says Poisson is best for large p too. Is there a contradiction here, or is Hoel just nuancing it as he goes along?

It turns out that for very large n there are two well-known density functions that give good approximations to the binomial density function: one when p is very small and the other when this is not the case. The approximation that applies when p is very small is known as the Poisson density function and it defines the Poisson distribution.

- Hoel: Introduction to Mathematical Statistics, 5th ed., p. 64.

In 2.5.1. the Poisson distribution was introduced as an approximation to the binomial distribution when n is large and p is small. It was stated that another distribution gives a good approximation for large n when p is not small. The normal distribution is the distribution with this property.

- Hoel: ibid., p. 81.

The two approximations that have been considered for the binomial distribution, namely the Poisson and normal distributions, are sufficient to permit one to solve all the simpler problems that require the computation of binomial probabilities. In n is small, one uses formula (11) [the binomial density function] directly because the computations are then quite easy. [...] If n is large and p is small or large, the Poisson approximation may be used. In n is large and p is not small or large, the normal approximation may be used. Thus all probabilities have been covered.

- Hoel: ibid., p. 85.
 
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I believe what is intended here is the Poisson distribution works when either p or (1-p) is small, which is what is meant by p small or large. The normal distribution is favored when p~=0.5.

So, "nuance" it is.
 
The Poisson distribution is a good approximation to the binomial when p << 1. If p is large (i.e., 1-p << 1), then N-n (N = # trials, n = # successes) will follow a Poisson distribution. This is what he means by saying the Poisson "may be used" -- not that n will follow a Poisson, but that you can use the Poisson to calculate the distribution of n. The normal distribution is a good approximation when both Np and N(1-p) are large. Hoel doesn't say this, but when all three conditions are met, p << 1, Np >> 1, and N(1-p) >> 1, both the Poisson and the normal are good approximations. I.e., the normal works even for small p or large p as long as N is big enough to compensate.
 
Excellent! Thanks for clearing that up for me.
 

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