# Discrete Probability Distribution

## Binomial Distribution 2

In this post, we will learn about Binomial Distribution and its basics.

A statistical experiment having successive independent trials having two possible outcomes (such as success and failure; true and false; yes and no; right and wrong etc.) and probability of success is equal for each trial, while this kind of experiment is repeated a fixed number of times (say $n$ times) is called Binomial Experiment, Each trial of this Binomial experiment is known as Bernoulli trial (a trial which is a single performance of an experiment), for example.

### Properties of the Binomial Experiment

1. Each trial of the Binomial Experiment can be classified as a success or failure.
2. The probability of success for each trial of the experiment is equal.
3. Successive trials are independent, that is, the occurrence of one outcome in an experiment does not affect the occurrence of the other.
4. The experiment is repeated a fixed number of times.

### Binomial Distribution

Let $X$ be a discrete random variable, which denotes the number of successes of a Binomial Experiment (we call this binomial random variable). The random variable assumes isolated values as $X=0,1,2,\cdots,n$. The probability distribution of the binomial random variables is termed binomial distribution. It is a discrete probability distribution.

### Binomial Probability Mass Function

The probability function of the binomial distribution is also called binomial probability mass function and can be denoted by $b(x, n, p)$, that is, a binomial distribution of random variable $X$ with $n$ (given number of trials) and $p$ (probability of success) as parameters. If $p$ is the probability of success (alternatively $q=1-p$ is probability of failure such that $p+q=1$) then probability of exactly $x$ success can be found from the following formula,

\begin{align}
b(x, n, p) &= P(X=x)\\
&=\binom{n}{x} p^x q^{n-x}, \quad x=0,1,2, \cdots, n
\end{align}

where $p$ is the probability of success of a single trial, $q$ is the probability of failure and $n$ is the number of independent trials.

The formula gives the probability for each possible combination of $n$ and $p$ of a binomial random variable $X$. Note that it does not give $P(X <0)$ and $P(X>n)$. The binomial distribution is suitable when $n$ is small and is applied when sampling done is with replacement.

$b(x, n, p) = \binom{n}{x} p^x q^{n-x}, \quad x=0,1,2,\cdots,n,$

is called Binomial distribution because its successive terms are exactly the same as that of binomial expansion of

\begin{align}
(q+p)^n=\binom{0}{0} p^0 q^{n-0}+\binom{n}{1} p^1 q^{n-1}+\cdots+\binom{n}{n-1} p^n q^{n-(n-1)}+\binom{n}{n} p^n q^{n-n}
\end{align}

$\binom{n}{0}, \binom{n}{1}, \binom{n}{2},\cdots, \binom{n}{n-1}, \binom{n}{n}$ are called Binomial coefficients.

Note that it is necessary to describe the limit of the random variable otherwise, it will be only the mathematical equation, not the probability distribution.

Read more about Binomial Probability Distribution and Take Online MCQ tests on Probability Distributions

Online MCQs Quiz Website

Generate Binomial Random Numbers in R Language

## Binomial Probability Distribution

To learn about Binomial Probability Distribution, we first need to understand the Bernoulli Trials.

Bernoulli Trials

Many experiments consist of repeated independent trials and each trial has only two possible outcomes such as head or tail, right or wrong, alive or dead, defective or non-defective, etc. If the probability of each outcome remains the same (constant) throughout the trials, then such trials are called the Bernoulli Trials.

### Binomial Probability Distribution

Binomial Probability Distribution is a discrete probability distribution describing the results of an experiment known as the Bernoulli Process. The experiment having n Bernoulli trials is called a Binomial Probability experiment possessing the following four conditions/ assumptions

1. The experiment consists of $n$ repeated tasks.
2. Each trial results in an outcome that may be classified as success or failure.
3. The probability of success denoted by $p$ remains constant from trial to trial.
4. The repeated trials are independent.

A Binomial trial can result in a success with probability p and a failure with probability $1-p$ having $n-x$ number of failures, then the probability distribution of Binomial Random Variable, the number of successes in $n$ independent trial is:

\begin{align*}
P(X=x)&=\binom{n}{x} \, p^x \, q^{n-x} \\
&=\frac{n!}{x!(n-x)!}\, p^x \, q^{n-x}
\end{align*}

The Binomial probability distribution is the most widely used distribution in situations of two outcomes. It was discovered by the Swiss mathematician Jakob Bernoulli (1654—1704) whose main work on “the ars Conjectandi” (the art of conjecturing) was published posthumously in Basel in 1713.

Mean of Binomial Distribution:   Mean = $\mu = np$

Variance of Binomial Distribution:  Variance = $npq$

Standard Deviation of Binomial Distribution:  Standard Deviation = $\sqrt{npq}$

Moment Coefficient of Skewness:

\begin{align*}
\beta_1 &= \frac{q-p}{\sqrt{npq}}  \\
&= \frac{1-2p}{\sqrt{npq}}
\end{align*}

Moment Coefficient of Kurtosis:  $\beta_3 = 3+\frac{1-6pq}{npq}$

Generate Binomial Random Numbers in R Language

Scroll to Top