The Binomial Distribution

"Bi" means "two" (like a bicycle has two wheels) ...
... so this is about things with two results.

   

 

Tossing a Coin:

  • Did we get Heads (H) or
  • Tails (T)

We say the probability of the coin landing H is ½
And the probability of the coin landing T is ½

  die

Throwing a Die:

  • Did we get a four ... ?
  • ... or not?

We say the probability of a four is 1/6 (one of the six faces is a four).
And the probability of not four is 5/6 (five of the six faces are not a four)

Let's Toss a Coin!

Toss a fair coin three times ... what is the chance of getting two Heads?

We will use these terms:

  • Outcome: the result of three coin tosses
  • Event: "Two Heads" out of three coin tosses

Tosing a coin three times could get any one of these outcomes (H is for heads and T for Tails):

HHH  
HHT  
HTH  
HTT  
THH  
THT  
TTH  
TTT  

Which outcomes do we want?

"Two Heads" could be in any order: "HHT", "THH" and "HTH" all have two Heads (and one Tail).

So 3 of the outcomes produce "Two Heads".

What is the probability of each outcome?

Each outcome is equally likely, and there are 8 of them. So each has a probability of 1/8

So the probability of event "Two Heads" is:

Number of
outcomes we want
    Probability of
each outcome
 
3   ×   1/8   =  3/8

Let's Calculate Them All:

The calculations are (P means "Probability of"):

  • P(Three Heads) = P(HHH) = 1/8
  • P(Two Heads) = P(HHT) + P(HTH) + P(THH) = 1/8 + 1/8 + 1/8 = 3/8
  • P(One Head) = P(HTT) + P(THT) + P(TTH) = 1/8 + 1/8 + 1/8 = 3/8
  • P(Zero Heads) = P(TTT) = 1/8

We can write this in terms of a Random Variable, X, = "The number of Heads from 3 tosses of a coin":

  • P(X = 3) = 1/8
  • P(X = 2) = 3/8
  • P(X = 1) = 3/8
  • P(X = 0) = 1/8

And we can also draw a Bar Graph:


It is symmetrical!

Making a Formula

Now ... what are the chances of 5 heads in 9 tosses ... to list all outcomes (512) would take a long time!

So let's make a formula.

 

In our previous example, how could we get the values 1, 3, 3 and 1 ?

 

They are actually in the third row of Pascal’s Triangle ... !

 

Can we make them using a formula?

Sure we can, and here it is:

 
   
  • n = total number
  • k = number we want

It is often called "n choose k" and you can read more
about it at Combinations and Permutations.

Note: the "!" means "factorial", for example 4! = 1×2×3×4 = 24

Let's use it:

Example: 3 tosses getting 2 Heads

We have n=3 and k=2

n!   =   3!   =   3×2×1   = 3
k!(n-k)! 2!(3-2)! 2×1 × 1

So there are 3 outcomes for "2 Heads"

(We knew that already, but now we have a formula for it.)

 

Let's use it for a harder question:

Example: what are the chances of 5 heads in 9 tosses?

We have n=9 and k=5

n!   =   9!   =   9×8×7×6×5×4×3×2×1   = 126
k!(n-k)! 5!(9-5)! 5×4×3×2×1 × 4×3×2×1

 

 

And for 9 tosses there are 29 = 512 total outcomes, so we get the probability:

 

Number of
outcomes we want
  Probability of
each outcome
   
126   ×   1   =   126
512 512

 

P(X=5)  =   126   =   63   = 0.24609375 
512 256

About a 25% chance.

(Easier than listing them all.)

Bias!

So far the chances of success or failure have been equally likely.

But what if the coins are biased (land more on one side than another) or choices are not 50/50.

Example: You sell sandwiches. 70% of people choose chicken, the rest choose pork.

What is the probability of selling 2 chicken sandwiches to the next 3 customers?

This is just like the heads and tails example, but with 70/30 instead of 50/50.

Let's draw a tree diagram:

The "Two Chicken" cases are highlighted.

Notice that the probabilities for "two chickens" all work out to be 0.147, because we are multiplying two 0.7s and one 0.3 in each case.

Can we get the 0.147 from a formula? What we want is "two 0.7s and one 0.3"

  • 0.7 is the probability of each choice we want, call it p
  • 2 is the number of choices we want, call it k
Probability of "choices we want" (two chickens) is: pk

And

  • The probability of the opposite choice is: 1-p
  • The total number of choices is: n
  • The number of opposite choices is: n-k
Probability of "opposite choices" (one pork) is: (1-p)(n-k)

So all choices together is:

pk(1-p)(n-k)

Example: (continued)

  • p = 0.7 (chance of chicken)
  • n = 3
  • k = 2

So we get:

pk(1-p)(n-k) = 0.72(1-0.7)(3-2) = 0.72(0.3)(1) = 0.7 × 0.7 × 0.3 = 0.147

which is the probability of each outcome.

 

And the total number of those outcomes is:

n!   =   3!   =   3×2×1   = 3
k!(n-k)! 2!(3-2)! 2×1 × 1

 

And we get:

Number of
outcomes we want
    Probability of
each outcome
   
3   ×   0.147   =   0.441

 

So the probability of event "2 people out of 3 choose chicken" = 0.441

 

OK. That was a lot of work for something we knew already, but now we can answer harder questions.

Example: You say "70% choose chicken, so 7 of the next 10 customers should choose chicken" ... what are the chances you are right?

  • p = 0.7
  • n = 10
  • k = 7

So we get:

pk(1-p)(n-k) = 0.77(1-0.7)(10-7) = 0.77(0.3)(3) = 0.0022235661

That is the probability of each outcome.

 

And the total number of those outcomes is:

n!   =   10!   =   10×9×8   = 120
k!(n-k)! 7!(10-3)! 3×2×1

 

And we get:

Number of
outcomes we want
    Probability of
each outcome
   
120   ×   0.0022235661   =   0.266827932

 

In fact the probability of 7 out of 10 choosing chicken is only about 27%

 

Moral of the story: even though the long-run average is 70%, don't expect 7 out of the next 10.

Putting it Together

Now we know how to calculate how many:

n!
k!(n-k)!

And the probability of each:

pk(1-p)(n-k)

We can multiply them together:

Probability of k out of n ways:

P(k out of n) =   n!   pk(1-p)(n-k)
k!(n-k)!

The General Binomial Probability Formula

Important Notes:

  • The trials are independent,
  • There are only two possible outcomes at each trial,
  • The probability of "success" at each trial is constant.

Quincunx

 

Have a play with the Quincunx (then read Quincunx Explained) to see the Binomial Distribution in action.

die

Throw the Die

A fair die is thrown four times. Calculate the probabilities of getting:

  • 0 Twos
  • 1 Two
  • 2 Twos
  • 3 Twos
  • 4 Twos

In this case n=4, p = P(Two) = 1/6

X is the Random Variable ‘Number of Twos from four throws’.

Substitute x = 0 to 4 into the formula:

P(k out of n) =   n! pk(1-p)(n-k)
k!(n-k)!

Like this (to 4 decimal places):

  • P(X = 0) = (4!/0!4!) × (1/6)0(5/6)4 = 1 × 1 × (5/6)4 = 0.4823
  • P(X = 1) = (4!/1!3!) × (1/6)1(5/6)3 = 4 × (1/6) × (5/6)3 = 0.3858
  • P(X = 2) = (4!/2!2!) × (1/6)2(5/6)2 = 6 × (1/6)2 × (5/6)2 = 0.1157
  • P(X = 3) = (4!/3!1!) × (1/6)3(5/6)1 = 4 × (1/6)3 × (5/6) = 0.0154
  • P(X = 4) = (4!/4!0!) × (1/6)4(5/6)0 = 1 × (1/6)4 × 1 = 0.0008

Summary: "for the 4 throws, there is a 48% chance of no twos, 39% chance of 1 two, 12% chance of 2 twos, 1.5% chance of 3 twos, and a tiny 0.08% chance of all throws being a two (but it still could happen!)"

This time the Bar Graph is not symmetrical:


It is not symmetrical!

It is skewed because p is not 0.5

 

Sports Bikes

Your company makes sports bikes. 90% pass final inspection (and 10% fail and need to be fixed).

What is the expected Mean and Variance of the 4 next inspections?

First, let's calculate all probabilities.

  • n = 4,
  • p = P(Pass) = 0.9

X is the Random Variable "Number of passes from four inspections".

Substitute x = 0 to 4 into the formula:

P(k out of n) =   n! pk(1-p)(n-k)
k!(n-k)!

Like this:

  • P(X = 0) = (4!/0!4!) × 0.900.14 = 1 × 1 × 0.0001 = 0.0001
  • P(X = 1) = (4!/1!3!) × 0.910.13 = 4 × 0.9 × 0.001 = 0.0036
  • P(X = 2) = (4!/2!2!) × 0.920.12 = 6 × 0.81 × 0.01 = 0.0486
  • P(X = 3) = (4!/3!2!) × 0.930.11 = 4 × 0.729 × 0.1 = 0.2916
  • P(X = 4) = (4!/4!0!) × 0.940.10 = 1 × 0.6561 × 1 = 0.6561

Summary: "for the 4 next bikes, there is a tiny 0.01% chance of no passes, 0.36% chance of 1 pass, 5% chance of 2 passes, 29% chance of 3 passes, and a whopping 66% chance they all pass the inspection."

Mean, Variance and Standard Deviation

Let's calculate the Mean, Variance and Standard Deviation for the Sports Bike inspections.

There are (relatively) simple formulas for them. They are a little hard to prove, but they do work!

The mean, or "expected value", is:

μ = np

For the sports bikes:

μ = 4 × 0.9 = 3.6

So we would expect 3.6 bikes (out of 4) to pass the inspection.
Makes sense really ... 0.9 chance for each bike times 4 bikes equals 3.6

The formula for Variance is:

Variance: σ2 = np(1-p)

And Standard Deviation is the square root of variance:

σ = √(np(1-p))

For the sports bikes:

Variance: σ2 = 4 × 0.9 × 0.1 = 0.36

Standard Deviation is:

σ = √(0.36) = 0.6

 

Note: we could also calculate them manually, by making a table like this:

X P(X) X × P(X) X2 × P(X)
0 0.0001 0 0
1 0.0036 0.0036 0.0036
2 0.0486 0.0972 0.1944
3 0.2916 0.8748 2.6244
4 0.6561 2.6244 10.4976
  SUM: 3.6 13.32

The mean is the Sum of (X × P(X)):

μ = 3.6

The variance is the Sum of (X2 × P(X)) minus Mean2:

Variance: σ2 = 13.32 − 3.62 = 0.36

Standard Deviation is:

σ = √(0.36) = 0.6

And we got the same results as before (yay!)

 

Summary

The General Binomial Probability Formula

P(k out of n) =   n! pk(1-p)(n-k)
k!(n-k)!

Mean value of X: μ = np

Variance of X: σ2 = np(1-p)
Standard Deviation of X: σ = √(np(1-p))