The Hypergeometric Probability Distribution is a fundamental concept in statistics that describes the probability of *k* successes (draws of a particular type) in *n* draws, without replacement, from a finite population of size *N* that contains exactly *K* successes. This distribution is particularly useful in scenarios where sampling is done without replacement, such as in quality control, medical testing, and various other fields where the total population is limited and each draw affects the remaining population.
Understanding the Hypergeometric Probability Distribution
The Hypergeometric Probability Distribution is defined by four parameters:
- *N*: The total number of items in the population.
- *K*: The number of successes in the population.
- *n*: The number of draws (or samples) taken from the population.
- *k*: The number of successes in the sample.
The probability mass function (PMF) of the Hypergeometric Distribution is given by:
📝 Note: The PMF formula is as follows:
P(X = k) = <
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