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Finite Population

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Honors Statistics

Definition

A finite population refers to a population that has a known, limited, and countable number of elements or members. This is in contrast to an infinite population, which has an unlimited or uncountable number of elements. The concept of a finite population is particularly relevant in the context of the hypergeometric distribution, where the sampling is done without replacement from a finite population.

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5 Must Know Facts For Your Next Test

  1. In a finite population, the size of the population is known and fixed, unlike an infinite population where the size is unknown or unbounded.
  2. The hypergeometric distribution is used to model the number of successes in a sample drawn without replacement from a finite population.
  3. Sampling without replacement is a key characteristic of the hypergeometric distribution, as each element drawn from the population is not returned, reducing the population size for subsequent draws.
  4. The population size is a crucial parameter in the hypergeometric distribution, as it determines the probabilities of the number of successes in the sample.
  5. Finite populations are often encountered in real-world scenarios, such as quality control, medical studies, and social surveys, where the total number of elements is known and limited.

Review Questions

  • Explain the significance of a finite population in the context of the hypergeometric distribution.
    • The concept of a finite population is central to the hypergeometric distribution, which models the number of successes in a sample drawn without replacement from a population with a known, limited size. The finite nature of the population means that the probability of selecting a success (or failure) on each draw is not independent, as the removal of an element from the population affects the probabilities for subsequent draws. This distinguishes the hypergeometric distribution from other discrete probability distributions, such as the binomial distribution, which assume an infinite or at least a very large population size.
  • Describe how the population size affects the probabilities in the hypergeometric distribution.
    • The population size is a crucial parameter in the hypergeometric distribution, as it directly influences the probabilities of the number of successes in the sample. As the population size increases, the hypergeometric distribution approaches the binomial distribution, where the probability of success on each draw is independent. However, for smaller population sizes, the hypergeometric probabilities differ significantly from the binomial, reflecting the impact of sampling without replacement. The smaller the population size, the more pronounced the effect of the finite population on the probabilities, leading to a greater divergence from the binomial distribution.
  • Analyze the relationship between the finite population size and the sampling method in the context of the hypergeometric distribution.
    • The finite population size and the sampling method of without replacement are intrinsically linked in the hypergeometric distribution. The finite population size means that each draw from the population reduces the number of available elements, affecting the probabilities of subsequent draws. This sampling without replacement is a key characteristic of the hypergeometric distribution, distinguishing it from other distributions like the binomial, which assume sampling with replacement. The interplay between the finite population size and the sampling method is what gives the hypergeometric distribution its unique properties and makes it particularly suitable for modeling situations where the population size is known and limited, and sampling is done without replacement.

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