# Random Number Generator

## Random Number

# More Generators

# Random Number Generator

### What is a random number?

Random numbers are numbers which values are uniformly distributed over a defined interval or set, of which it is not possible to predict future values based on past or present ones. Random numbers are important in statistical analysis and probability theory. For more info read statistical randomness from Wikipedia.

### What is the most random number?

It is not possible to define a most random number since the probability of picking a random number is the same for all numbers. But it is demonstrated that it is possible to identify most picked random two-digit number: when groups of people are polled to pick a random number between 1 and 100, the most commonly chosen number is 37. The most picked number between 1 and 20 is usually 17. The most picked number between 1 and 10 are 3 and 7.

### How to generate a random number?

The simplest way is to use our tool.

You can also generate a random number manually using a pseudo random generator mathematical formula or try the following process: pick two complex numbers and perform a multiplication; then divide it for the time it took to perform the multiplication, measured with a chronometer. If you prefer to use Microsoft Excel, there are two worksheet functions that are meant to generate random numbers: rand and randbetween. =RANDBETWEEN(1,100) function returns a random integer between 1 and 100. =RAND() gives you a random real number greater than or equal to 0 and less than 1. You can find more info and examples on official office support pages.

### Is a random number generators truly random?

There are two principal methods used to generate random numbers. The first method measures some physical phenomenon that is expected to be random like atmospheric noise, thermal noise, electromagnetic phenomena or sources of natural entropy. Most computer generated random numbers use pseudorandom number generators (PRNGs) which are algorithms that can automatically create long runs of numbers with good random properties but not as good as physical methods. For example give some work to the processor like performing complex multiplications; then divide the result by the time it took to perform the multiplication.

### Probability, random number analysis

Let’s start simple with some probability examples.

The probability of guessing correctly a number between 1 and 10 is 1/10 (10%)

The probability of guessing correctly a number between 1 and 20 is 1/20 (5%)

The probability of guessing correctly a number between 1 and 100 is 1/100 (1%)

The probability of guessing correctly 3 numbers between 1 and 100 is 1/100 x 1/100 x 1/100 = 1/1,000,000 (0.0001%)

This calculation is valid every time that the extractions are unpredictable and independent. But in the case of multiple choice answers, card games or lotteries it is different because the independence condition is no longer verified: a number cannot be selected again if already occurred. This way you can calculate the probability to win the lottery: if you have to guess correctly 6 numbers on 90 your probability is 6/90 for the first extraction, 5/89 for the second, 4/88 for the third, 3/87 for the fourth, 2/86 for the fifth and 1/85 for the sixth number. The total probability is 6/90 x 5/89 x 4/88 x 3/87 x 2/86 x 1/85 = 720/448,282,533,600 =1/622,614,630