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    Binomial distribution summarizes the probability of a value taking one or two independent values and by considering a set of assumptions and parameters. The following assumptions are made in binomial distribution; There is only one outcome for each trial Every trial made has an equal chance of success and failure, Each trial is mutually exclusive and independent to each other. The experiment is performed under the same conditions for a fixed number of trials

    Binomial Distribution

    A researcher uses binomial distribution to observe a specified number of successful outcomes in a set number of trials. The expected value is calculated by multiplying the number of trials by the probability of successes. Binomial distribution theorem was propounded by a Swiss mathematician, Jacob Bernoulli in 1700. A researcher would carry out a repeated experiment which either yields a success or failure. The occurrence of failure or success in the experiment has no pattern.

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    Binomial distribution also known as Bernoulli distribution, is a probability distribution that determines the chance of success or failure occurring in an experiment. It is greatly applied in many disciplines such as business and social sciences to describe a wide variety of processes. Binomial distribution has three main attributes;

    Pattern or shape of the binomial distribution

    The standard deviation which measures the dispersion of the binomial distribution

    The mean of the binomial distribution which is denoted by (nP)

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