Negative hypergeometric distribution

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Negative hypergeometric

N \in \left\{0,1,2,\dots\right\} - total number of elements
K \in \left\{0,1,2,\dots,N\right\} - total number of 'success' elements

R \in \left\{0,1,2,\dots,N-K\right\} - number of failures when experiment is stopped
Support k \in \left\{0,\, \dots,\, K\right\} - number of successes when experiment is stopped.
Mean R\frac{K}{N-K+1}
Variance R\frac{(N+1)K}{(N-K+1)(N-K+2)}[1-\frac{R}{N-K+1}]

In probability theory and statistics, the negative hypergeometric distribution describes probabilities for when sampling from a finite population without replacement in which each sample can be classified into two mutually exclusive categories like Pass/Fail, Male/Female or Employed/Unemployed. As random selections are made from the population, each subsequent draw decreases the population causing the probability of success to change with each draw. Unlike the standard hypergeometric distribution, which describes the number of successes in a fixed sample size, in the negative hypergeometric distribution, samples are drawn until R failures have been found, and the distribution describes the probability of finding k successes in such a sample. In other words, the negative hypergeometric distribution describes the likelihood of k successes in a sample with exactly R failures.


There are N elements, of which K are defined as "successes" and the rest are "failures".

Elements are drawn one after the other, without replacements, until R failures are encountered. Then, the drawing stops and the number k of successes is counted. The negative hypergeometric distribution, NHG_{N,K,R}(k) is the discrete distribution of this k.


Related distributions[edit]

If the drawing stops after a constant number n of draws (regardless of the number of failures), then the number of successes has the hypergeometric distribution, HG_{N,K,n}(k). The two functions are related in the following way:[1]

NHG_{N,K,R}(k) = 1-HG_{N,N-K,k}(R-1)

Negative-hypergeometric distribution (like the hypergeometric distribution) deals with draws without replacement, so that the probability of success is different in each draw. In contrast, negative-binomial distribution (like the binomial distribution) deals with draws with replacement, so that the probability of success is the same and the trials are independent. The following table summarizes the four distributions related to drawing items:

With replacements No replacements
Constant number of draws binomial distribution hypergeometric distribution
Constant number of failures negative binomial distribution negative hypergeometric distribution


  1. ^ a b Negative hypergeometric distribution in Encyclopedia of Math.