Lesson 2 The Factorial

Motivating Example

How many ways are there to arrange a deck of 52 cards?


We can use the multiplication rule to determine the number of ways to arrange the deck. The first card can be any one of 52 cards. No matter which one it is, the second card can be any one of the remaining 51 cards. So there are \(52 \cdot 51\) ways to choose the first 2 cards. For every one of these \(52 \cdot 51\) ways, there are \(50\) remaining cards to choose as the third card, which makes \(52 \cdot 51 \cdot 50\) ways to choose the first 3 cards. And so on. By the time we get to the last card in the deck, there is only \(1\) card left. So there are \[ 52 \cdot 51 \cdot 50 \cdot \ldots \cdot 2 \cdot 1 \] ways to arrange the 52 cards in a deck.

This is such an important quantity in probability and counting that it has been given a special name.
Definition 2.1 (Factorial) The quantity \(n!\) (pronounced: “n factorial”) is defined as \[ n! = n \cdot (n-1) \cdot \ldots \cdot 1. \] It represents the number of ways to arrange \(n\) objects.

So the number of ways to arrange a deck of cards can be expressed as \(52!\). Using Wolfram Alpha, we can calculate this to be about \(8 \times 10^{67}\), an astronomical number. In particular, it is greater than:

  • the number of seconds since the universe began.
  • the number of atoms on Earth.

So the next time you hold a shuffled deck of cards in your hands, spend a moment appreciating the fact that you are holding an arrangement that has likely never before existed in the history of the universe.


  1. Each year, as part of a “Secret Santa” tradition, a group of 4 friends write their names on slips of papers and place the slips into a hat. Each member of the group draws a name at random from the hat and must by a gift for that person. Of course, it is possible that they draw their own name, in which case they buy a gift for themselves. What is the probability that everyone in the group ends up buying a gift for themselves? (Note that the names are not placed back in the hat once they are drawn, so each person receives exactly one gift.)
  2. A deck of 52 cards is shuffled thoroughly. What is the probability that the four aces are all next to each other? (Hint: First, count the number of positions that the block of four aces can go, then multiply this by the number of ways of ordering the four aces.)
  3. If a five-letter word is formed at random (meaning that all sequences of five letters are equally likely), what is the probability that no letter occurs more than once?
  4. The “bootstrap” is a statistical method for generating a new data set that is like an existing one. Suppose we have a data set consisting of \(6\) observations: \(x_1, x_2, x_3, x_4, x_5, x_6\). To generate a “bootstrap” data set, we sample from the original data set with replacement, meaning that it is possible for each observation to be sampled more than once. Examples of bootstrap data sets include: \[\begin{align*} x_4, x_2, x_4, x_3, x_2, x_4 \\ x_3, x_1, x_6, x_1, x_1, x_2 \\ x_2, x_1, x_4, x_3, x_6, x_5 \end{align*}\] Notice that in the last example, each observation appears exactly once. What is the probability that the bootstrap data set contains each observation exactly once?

Additional Exercises