How fast does n! grow? Type any integer from 0 to 170 and get the exact factorial value, the expanded multiplication and scientific notation in one click.
Factorial Calculator - Compute n! Instantly
How fast does n! grow? Type any integer from 0 to 170 and get the exact factorial value, the expanded multiplication and scientific notation in one click.
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What is a factorial?
The factorial of a non-negative integer n, written as n!, is the product of all positive integers from 1 to n. For example, 5! = 1 x 2 x 3 x 4 x 5 = 120. By convention, 0! = 1.
Factorial values from 0! to 20!
| n | n! | Digits | Context |
|---|---|---|---|
| 0 | 1 | 1 | By definition (empty product) |
| 1 | 1 | 1 | Base case |
| 5 | 120 | 3 | Ways to arrange 5 books on a shelf |
| 10 | 3,628,800 | 7 | Ways to seat 10 people around a table |
| 13 | 6,227,020,800 | 10 | First factorial exceeding 1 billion |
| 20 | 2,432,902,008,176,640,000 | 19 | Largest factorial fitting in a 64-bit integer |
| 52 | 8.07e+67 | 68 | Ways to shuffle a deck of cards |
| 100 | 9.33e+157 | 158 | More than atoms in the observable universe |
| 170 | 7.26e+306 | 307 | Largest factorial in IEEE 754 double |
Practical examples
Permutations of n distinct objects = n!
C(n,k) = n! / (k! x (n-k)!)
52! has 68 digits - incomprehensibly large
Permutations with repetition: n! / (r1! x r2! x ...)
The empty product convention: product of no factors = 1
Where factorials appear
| Formula | Name | What it counts |
|---|---|---|
| n! | Permutations | Ways to arrange n distinct objects in a row |
| n! / (n-k)! | k-permutations | Ways to pick and arrange k items from n |
| n! / (k!(n-k)!) | Combinations C(n,k) | Ways to choose k items from n (order irrelevant) |
| e^x = sum x^n/n! | Taylor series | Approximating functions as infinite polynomials |
| e^(-m) m^k / k! | Poisson distribution | Probability of k events in a fixed interval |
Stirling's approximation
For large n, computing n! exactly becomes impractical. Stirling's formula gives a close estimate:
For n = 10: Stirling gives 3,598,696 vs exact 3,628,800 - an error of only 0.83%. At n = 100 the relative error drops below 0.08%. This approximation is widely used in statistical mechanics and information theory.
FAQ - Frequently Asked Questions
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