Relationship b/w Binomial, CLT & Poisson Distrib.

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SUMMARY

The discussion clarifies the relationship between the binomial distribution, normal distribution, and Poisson distribution, particularly in the context of the Central Limit Theorem (CLT). It establishes that the binomial distribution can be approximated by a normal distribution N(0,1) when the probability p remains constant as n approaches infinity. Conversely, the Poisson distribution is derived from the binomial distribution when the product np remains constant as n approaches infinity. This distinction highlights the different limiting processes involved in these approximations.

PREREQUISITES
  • Understanding of the Central Limit Theorem (CLT)
  • Familiarity with binomial distribution concepts
  • Knowledge of normal distribution properties
  • Comprehension of Poisson distribution characteristics
NEXT STEPS
  • Study the Central Limit Theorem in detail
  • Explore the properties of binomial distributions
  • Learn about the derivation of the Poisson distribution from the binomial distribution
  • Investigate the implications of approximation methods in statistical analysis
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Statisticians, data analysts, and students of probability theory seeking to understand the connections between different statistical distributions and their applications in real-world scenarios.

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From the central limit theorem the binomial distribution can be approximated by a normal distribution N(0,1). But the binomial distribution can also be approximated by a poisson distribition.

Does this mean there is a relationship between the normal distribution and the poisson distribution (especially as n->infinity in B(n,p))?

I'm confused about this. To me it sounds like the normal distribution and the poisson distribution are equal then. Or does the standardisation in the central limit theorem make the difference?

Thanks
 
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The difference depends on how you take the limit. To get a normal from binomial, you keep p constant as n -> ∞. To get a Poisson from a binomial, you keep np constant as n -> ∞.
 

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