Binomial distribution

Binomial distribution pmf

Binomial distribution cdf

Pascal's triangle; binomial distribution

Binomial Distribution

Binomial Distribution is a probability distribution that summarizes the likelihood that a value will take on one of two independent values under a given set of parameters or assumptions. The concept is widely used in statistics, probability theory, and various fields that involve decision making under uncertainty, such as finance, healthcare, and engineering.

Definition

The binomial distribution is defined by two parameters: \(n\) and \(p\). Here, \(n\) represents the number of trials, and \(p\) represents the probability of success on an individual trial. The random variable \(X\), which follows a binomial distribution, represents the number of successes in \(n\) trials.

The probability mass function (PMF) of a binomial distribution is given by:

\[ P(X = k) = \binom{n}{k} p^k (1-p)^{n-k} \]

where \( \binom{n}{k} \) is the binomial coefficient, calculated as \( \frac{n!}{k!(n-k)!} \), \(k\) is the number of successes, \(n-k\) is the number of failures, \(p\) is the probability of success, and \(1-p\) is the probability of failure.

Characteristics

Mean

The mean, or expected value, of a binomial distribution is given by \( \mu = np \).

Variance

The variance of a binomial distribution is given by \( \sigma^2 = np(1-p) \).

Standard Deviation

The standard deviation is the square root of the variance, \( \sigma = \sqrt{np(1-p)} \).

Applications

Binomial distributions are used in a variety of fields to model binary outcomes. For example, in healthcare, it can be used to model the probability of a certain number of patients recovering from a disease out of a total number of cases. In quality control, it can model the number of defective items in a batch of products.

Examples

1. Coin Toss: If a fair coin is tossed 10 times, the probability of getting exactly 6 heads can be calculated using the binomial distribution with \(n=10\) and \(p=0.5\).

2. Quality Control: If a factory produces items with a 2% defect rate, the probability of finding exactly 5 defective items in a sample of 100 can be calculated using the binomial distribution with \(n=100\) and \(p=0.02\).

Limitations

While the binomial distribution is widely applicable, it has limitations. It assumes a fixed number of trials, a constant probability of success, and independent trials. When these assumptions do not hold, other distributions, such as the Poisson distribution or the negative binomial distribution, may be more appropriate.

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