Probability Density Function (PDF)

Probability density function (PDF) for the Hypergeometric Distribution is a mathematical function that describes the probability of observing a specific value ‘x’ (the number of successes) in a sample of size ‘n’ drawn from a finite population of size ‘N’ containing ‘k’ successes. It can be described as:

Here,

  • P(X = x) represents the probability of obtaining ‘x’ successes.
  • is the number of ways to choose ‘x’ successes from ‘k’.
  • is the number of ways to choose ‘n – x’ failures from ‘N – k’.
  • is the total number of ways to choose ‘n’ items from a population of ‘N’.

Hypergeometric Distribution in Business Statistics : Meaning, Examples & Uses

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What is Hypergeometric Distribution?

Hypergeometric Distribution is a discrete probability distribution that models the probability of drawing a particular number of successes (usually classified as ‘x’) without an alternative from a finite population of items, containing each success and failure. In simpler terms, it is used to calculate the probability of obtaining a certain number of successful outcomes from a finite population while every draw affects the probability of the next draw. This makes it particularly valuable in scenarios where the population size is small, and sampling is finished without replacement....

Probability Density Function (PDF)

Probability density function (PDF) for the Hypergeometric Distribution is a mathematical function that describes the probability of observing a specific value ‘x’ (the number of successes) in a sample of size ‘n’ drawn from a finite population of size ‘N’ containing ‘k’ successes. It can be described as:...

Mean and Variance

I. Mean: Mean calculates the average number of successful outcomes you can expect to obtain in a sample of size n, family population of N with k successful outcomes. It is given by the formula,...

Examples of Hypergeometric Distribution

Example 1: Employee Promotion...

When to Use the Hypergeometric Distribution?

The Hypergeometric Distribution is suitable in situations wherein:...

Difference between Hypergeometric Distribution and Binomial Distribution

Basis Hypergeometric Distribution Binomial Distribution Population Size Finite and sample without replacementFinite or Infinite and With or without replacementDependency of Trials Non-DependentDependentFormula Involves combination of Binomial coefficientsSimple Probability formulaUse Cases Quality control and Population analysiscoin flips and product defectsParameters N, k, n and xn, p...

Conclusion

In the realm of business statistics, Hypergeometric Distribution performs an essential position in studying and predicting consequences when dealing with finite populations and non-independent events. By understanding its probability density function, mean, and variance, we gain a precious tool for making informed decisions in various situations. Comparing the Hypergeometric Distribution with the Binomial Distribution highlights their awesome capabilities and packages. While the Hypergeometric Distribution is applicable for non-unbiased, finite population eventualities, the Binomial Distribution is more flexible and applicable in a wide number of conditions....