Formula to Calculate Binomial Distribution
Binomial Distribution Formula is used to calculate probability of getting x successes in the n trials of the binomial experiment which are independent and the probability is derived by combination between number of the trials and number of successes represented by nCx is multiplied by probability of the success raised to power of number of successes represented by px which is further multiplied by probability of the failure raised to power of difference between number of success and number of the trials represented by (1-p) n-x.
The probability of obtaining x successes in n independent trials of a binomial experiment is given by the following formula of binomial distribution:
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For eg:
Source: Binomial Distribution Formula (wallstreetmojo.com)
where p is the probability of success
In the above equation, nCx is used, which is nothing but a combination formula. The formula to calculate combinations is given as nCx = n! / x! (n-x)! where n represents the number of items (independent trials), and x represents the number of items being chosen at a time (successes).
In case n=1 in a binomial distribution, the distribution is known as Bernoulli distribution. The mean of a binomial distribution is np. The variance of the binomial distribution is np(1-p).
Calculation of the Binomial Distribution (Step by Step)
The calculation of binomial distribution can be derived by using the following four simple steps:
- Calculate the combination between the number of trials and the number of successes. The formula for nCx is where n! = n*(n-1)*(n-2) . . . *2*1. For a number n, the factorial of n can be written as n! = n*(n-1)! For instance, 5! is 5*4*3*2*1
- Calculate the probability of success raised to the power of the number of successes that are px.
- Calculate the probability of failure raised to the power of the difference between the number of successes and the number of trials. The probability of failure is 1-p. Thus, this refers to obtaining (1-p) n-x
- Find out the product of the results obtained in Step 1, Step 2, and Step 3.
Examples
Example #1
The number of trials (n) is 10. The probability of success (p) is 0.5. Do the calculation of binomial distribution to calculate the probability of getting exactly six successes.
Solution:
Use the following data for the calculation of binomial distribution.
Calculation of binomial distribution can be done as follows,
P(x=6) = 10C6*(0.5)6(1-0.5)10-6
= (10!/6!(10-6)!)*0.015625*(0.5)4
= 210*0.015625*0.0625
Probability of Getting Exactly 6 Successes will be-
P(x=6) = 0.2051
The probability of getting exactly 6 successes is 0.2051
Example #2
A manager of an insurance company goes through the data of insurance policies sold by insurance salesmen working under him. He finds that 80% of the people who purchase motor insurance are men. He wants to find out that if 8 motor insurance owners are randomly selected, what would be the probability that exactly 5 of them are men.
Solution: We first have to find out what are n, p, and x.
Calculation of binomial distribution can be done as follows,
P(x=5) = 8C5*(0.8)5(1-0.8)8-5
= (8! /5! (8-5)! )*0.32768*(0.2)3
= 56*0.32768*0.008
Probability of Exactly 5 Successes will be-
P(x=5) = 0.14680064
The probability of exactly 5 motor insurance owners being men is 0.14680064.
Example #3
Hospital management is excited about the introduction of a new drug for treating cancer patients as the chance of a person being successfully treated by it is very high. The probability of a patient being successfully treated by the drug is 0.8. The drug is given to 10 patients. Find the probability of 9 or more patients being successfully treated by it.
Solution: We first have to find out what is n, p, and x.
We have to find the probability of 9 or more patients being successfully treated by it. Thus, either 9 or 10 patients are successfully treated by it
x (a number that you have to find a probability for) = 9 or x = 10
We have to find P(9) and P(10)
Calculation of binomial distribution to find P(x=9) can be done as follows,
P(x=9) = 10C9*(0.8)9(1-0.8)10-9
= (10! /9! (10-9)!)*0.134217728*(0.2)1
= 10*0.134217728*0.2
Probability of 9 Patients will be-
P(x=9) = 0.2684
Calculation of binomial distribution to find P(x=10) can be done as follows,
P(x=10) = 10C10*(0.8)10(1-0.8)10-10
= (10!/10! (10-10)!)*0.107374182*(0.2)0
= 1*0.107374182*1
The probability of 10 Patients will be-
P(x=10) = 0.1074
Therefore, P(x=9)+P(x=10) = 0.268 + 0.1074
= 0.3758
Thus, the probability of 9 or more patients being treated by the drug is 0.375809638.
Binomial Distribution Calculator
You can use the following binomial distribution calculator.
| n | |
| p | |
| x | |
| Binomial Distribution Formula = | |
| Binomial Distribution Formula = | nCx * px * (1 -p)n-x | |
| 0C0 * 00 * (1-0)0-0 = | 0 |
Relevance and Use
- There are only two outcomes
- The probability of each outcome remains constant from trial to trial
- There are a fixed number of trials
- Each trial is independent, i.e., mutually exclusive of others
- It provides us with the frequency distributionFrequency DistributionFrequency distribution refers to the repetitiveness of a variable, i.e., the number of times a variable occurs in a data set. In excel, it is a function to tabulate or graphically represent the recurrence of a particular value in a group or at an interval.read more of the possible number of successful outcomes in a given number of trials where each of these given trials has the same probability of success.
- Each trial in a binomial experiment can result in just two possible outcomes. Hence, the name is ‘binomial.’ One of these outcomes is known as success and the other as a failure. For instance, people who are sick may respond to a treatment or not.
- Similarly, when we toss a coin, we can have only two types of outcomes: heads or tails. The binomial distribution is a discrete distribution used in statisticsStatisticsStatistics is the science behind identifying, collecting, organizing and summarizing, analyzing, interpreting, and finally, presenting such data, either qualitative or quantitative, which helps make better and effective decisions with relevance.read more, which is different from a continuous distribution.
An example of a binomial experiment is tossing a coin, say thrice. When we flip a coin, only two outcomes are possible – heads and tails. The probability of each outcome is 0.5. Since the coin is tossed thrice, the number of trials is fixed, that is 3. The probability of each toss is not influenced by other tosses.
Binomial distribution finds its applications in social science statistics. It is used for developing models for dichotomous outcome variables where there are two outcomes. An example of this is whether Republicans or Democrats would win the election.
Binomial Distribution Formula in Excel (with excel template)
Saurabh learned about the binomial distribution equation in school. He wants to discuss the concept with his sister and have a bet with her. He thought that he would toss an unbiased coin ten times. He wants to bet $100 on getting exactly five tails in 10 tosses. For this bet, he wants to compute the probability of getting exactly five tails in 10 tosses.
Solution: We first have to find out what is n, p, and x.
There is an inbuilt formula for binomial distribution is Excel, which is
It is BINOM.DIST(number of successes, trials, a probability of success, FALSE).
For this example of the binomial distribution would be:
=BINOM.DIST(B2, B3, B4, FALSE) where cell B2 represents the number of successes, cell B3 represents the number of trials, and cell B4 represents the probability of success.
Therefore, the calculation of Binomial Distribution will be-
P(x=5) = 0.24609375
The probability of getting exactly 5 tails in 10 tosses is 0.24609375
Recommended Articles
This article has been a guide to the Binomial Distribution Formula. Here we learn how to calculate the probability of X using binomial distribution in excel with examples and a downloadable excel template. You can learn more about excel modeling from the following articles –
- Poisson Distribution Formula
- Examples of Normal Distribution Formula
- Formula of Standard Normal Distribution
- Sampling Distribution
