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Our normal approximation to the hypergeometrical distribution assignment help team always discusses the subject and the specific topic in details. The hypergeometric distribution is a discrete probability distribution that describes the probability of k successes in n binary trials. This event is done with replacement, from a finite population of size N that contains exactly K successes. If the population size is large enough then it is assumed that the above mentioned random variable will follow normal distribution. This is called normal approximation. Generally the cut-off for large number is used as 30. The theoretical support for this approximation is central limit theorem. Before big data analytics, people used this approximation for most of the statistical analysis.
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