## The Binomial Distribution Formula: Binomial Distribution Overview

The binomial distribution is a type of distribution in statistics that has **two possible outcomes** (the prefix “bi” means two, or twice). For example, a coin toss has only two possible outcomes: heads or tails and taking a test could have two possible outcomes: pass or fail.

Binomial distributions must also meet the following three criteria:

**The number of observations or trials is fixed.**In other words, you can only figure out the probability of something happening if you do it a certain number of times. This is common sense — if you toss a coin once, your probability of getting a tails is 50%. If you toss a coin a 20 times, your probability of getting a tails is very, very close to 100%.**Each observation or trial is**independent. In other words, none of your trials have an effect on the probability of the next trial.- The
**probability of success**(tails, heads, fail or pass) is**exactly the same**from one trial to another.

Once you know that your distribution is binomial, you can apply the **binomial distribution formula **to calculate the probability.

## The Binomial Distribution Formula

The binomial distribution formula is:

**b(x; n, P) = _{n}C_{x} * P^{x} * (1 – P)^{n – x}**

Where:

b = binomial probability

x = total number of “successes” (pass or fail, heads or tails etc.)

P = probability of a success on an individual trial

n = number of trials

**Note:** The binomial distribution formula can also be written in a slightly different way, because _{n}C_{x} = n!/x!(n-x)! (this binomial distribution formula uses factorials (What is a factorial?). “q” in this formula is just the probability of failure (subtract your probability of success from 1).

### Sample Problem Using the First Binomial Distribution Formula

Q. A coin is tossed 10 times. What is the probability of getting exactly 6 heads?

I’m going to use this formula: b(x; n, P) – _{n}C_{x} * P^{x} * (1 – P)^{n – x}

The number of trials (n) is 10

The odds of success (“tossing a heads”) is 0.5 (So 1-p = 0.5)

x = 6

P(x=6) = _{10}C_{6} * 0.5^6 * 0.5^4 = 210 * 0.015625 * 0.0625 = 0.205078125

**Tip:** You can use the **combinations calculator** to figure out the value for _{n}C_{x}.

## How to Work a Binomial Distribution Formula: Example #2

The binomial distribution formula can calculate the probability of success for binomial distributions. Often you’ll be told to “plug in” the numbers to the **formula and calculate**. This is easy to say, but not so easy to do–unless you are *very* careful with order of operations, you won’t get the right answer. If you have a Ti-83 or Ti-89, the calculator can do much of the work for you. If not, here’s how to break down the problem into simple steps so you get the answer right–every time.

**Step 1:**: Read the question carefully. Sample question: “80% of people who purchase pet insurance are women. If 9 pet insurance owners are randomly selected, find the probability that exactly 6 are women.”

**Step 2:**: Identify ‘n’ and ‘X’ from the problem. Using our sample question, n (the number of randomly selected items) is 9, and X (the number you are asked to find the probability for) is 6.

**Step 3:** Work the first part of the formula. The first part of the formula is

n! / (n – X)! X!

Substitute your variables:

9! / ((9 – 6)! × 6!)

Which equals 84. Set this number aside for a moment.

**Step 4:** Find p and q. p is the probability of success and q is the probability of failure. We are given p = 80%, or .8. So the probability of failure is 1 – .8 = .2 (20%).

**Step 5:** Work the second part of the formula.

p^{X}

= .8^{6}

= .262144

Set this number aside for a moment.

**Step 6:** Work the third part of the formula.

q^{(n – X)}

= .2^{(9-6)}

= .2^{3}

= .008

**Step 7:** Multiply your answer from step 3, 5, and 6 together.

84 × .262144 × .008 = 0.176.

That’s the simple explanation. For a more technical definition of the binomial and associated functions, check out the Wolfram page.

I love this

i love it….

good one! But pls, it would iron out complex examples such as prob of “altleast” combined with “or” & “and”.

Thank you so much. You simplified the formula where the probability of me remembering it is 100% success.

I found this page is helpful.

i love this please be in contact with me by my email i will need your more support

Hello, Nazir,

Please post on our forum; one of our mods will be happy to help!

Statistics Help Forum

I have slightly different problem to solve and thru this example i am not able to decide n x p etc

i have a 3695 balls of which 37 are red ,28 are blue 43 are yellow, When i take a sample of 100 , 1) what is the probability that NO red ball comes? and 2) ATLEAST 2 RED Balls OR ATLEAST 2 YELLOW balls will come? thank you

Hello Sudarshan,

Could you please post this on our forum?

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pls solve for me

An accountant is to audit 24 accounts of a firm. sixteen of these are of highly valued customer. if the accountant selects 4 of the accounts at roundom, what is the probability that he chooses at least one highly-valued account?

Hi, Sanndy,

Due to the overwhelming number of comments and emails I’m unable to answer every individual question about stats. But if you like, please post on our forum. One of our moderators will be happy to help!

How to calculat combination calculater

See the calculator here.

Thank u

Very helpful, thank u sir

The student body of a large university consists of 60% female students. A random sample of 8 students is selected. What is the probability that among the students in the sample at least 6 are female? Please solve it for me.

Hi, Sandy,

Just plug your numbers into the formula. Your p is .6, which would make your q .4.

Note that because it’s “at least 6” you would have to work the formula three times, for n = 6,7, and 8.

Then add all the probabilities up to get your answer.

this stuff is better than my textbook thank you guys so much

Why is the first part of the binomial probability formula the combination formula and not the permutation formula? Does order not matter? Why? For example, the probability that 3 out of 5 people have a blood type O. Does it not make a difference that persons 1, 3 and 5 have the O blood type rather than persons 1, 2, 3?

Check out this answer.

The mean and variance of a binomial distribution are 3 and 2 respectively. Find the probability that the number of successes are:

1) at most 2

2) at least 7.

Where do you get stuck? i.e. what is your working out so far? Post it and I’ll be happy to take a look.

Excellent it is so easy to understand

Confusion no more….

Plz explain 10C6÷=?

You need the combinations formula.