Random number generators automatically choose from a limited range of numbers with no predictable patterns when it comes to their creation. Each choice of the following number is entirely independent of the previous one. However, it is possible to specify a distribution range before generating a random number between those limits. This requires input from the user and is completely based on their requirements for randomization and intended outcome.

## Basic Random Number Generator

You can use our basic random number generator if you are looking for just one random number. First, however, you must decide what scope you will use for your number. The scope is the range of numbers from which you can generate the random number.

For example, if you want a random number between 1 and 10, your scope would be 1 – 10. To enter this into the calculator, input one as the lower limit and ten as the upper limit.

## Advanced Random Number Generator

Use the extended version of the random number generator if you want to generate more than one number or if you would like to deal with a much larger scope. Determine the scope for the lower and upper limits, then type in how many numbers you wish to generate.

You also have the option to generate either integers or decimals. Integers are also known as whole numbers, like 1, 2, and 3. Decimal numbers are numbers separated by a decimal separator (dot or comma) and would typically look like this: 1.02; 2.12; 3.33, etc.

There are a few other prompts available for our comprehensive random number generator. You can choose whether to allow duplication in the results, sort your results, and how many digits you want if you prefer decimals.

While it's ideal to be precise in most cases, some situations call for randomness. If you are looking for results no one can predict, you need a process that generates random results. That's where random number generators come in.

Random number generators have a wide range of applications and are used in industries like gaming, security, and lotteries – but you can also use them in the most mundane scenarios. In this guide, we will discuss what random number generators are, how they work, some of their most popular uses, and how they came to be.

## The Random Number Generator Defined

A random number generator chooses a random number or numbers based on the scope it is given. It can be hardware-based or pseudo-random.

Hardware random number generators (HRNG) rely on physical phenomena such as atmospheric noise, thermal noise, and other phenomena that, in theory, are not computable. Classic examples include coin flipping, a die, and a roulette wheel. More sophisticated devices are also used in the security and cryptography industries.

Pseudo-random number generators (PRNG) are algorithms that generate a sequence of numbers that approximate true randomness. They are often used in computer programs because they are faster and easier to implement than hardware-based random number generators. Our calculator is an example of a pseudo-random number generator.

## Problems the Random Number Generator Solves

A random number generator can be used in a variety of settings. You may already be using it in small situations without realizing it. If you have difficulty making a decision and resort to flipping a coin, you use a random number generator.

Many applications require some form of randomness, including games, simulations, and security. For example, a game may use a random number generator to select each player's next move or determine which cards are dealt to each player.

A simulation may use a random number generator to generate random numbers to use in its calculations. Security applications may use random number generators to generate one-time passwords or encryption keys.

## When to Use a Random Number Generator

The results from a random number generator can be handy in various scenarios, big or small. For instance, if you believe in the power of luck, you can use our calculator to choose your lottery numbers. If you're planning an event with raffle prizes involved, a random number generator can help you determine the winners.

You can use a random number generator when making statistical calculations on a larger scale.

If you want to know when to use a random number generator, here are the signs to look for:

- You would like to create a sense of chance in your game or application.
- You need to generate numbers that are hard to guess.
- You are working with a population that is too large to enumerate exhaustively.

## The History of the Random Number Generator

The history of the random number generator is shrouded in mystery. Some say that it was created by the ancient Chinese for divination. Others claim that Arab mathematicians first used it for gambling purposes.

Regardless of its origins, the random number generator has been used for centuries to create random results.

Dice, for instance, took on different forms and shapes in ancient times compared to the one we know today. Archeologists discovered dice made of different materials, like sticks, shells, bones, and dice with only 2 or 3 sides. The oldest known cubic dice were known to come from the Indus Valley around 2500 B.C.

The earliest recorded invention of an electronic random number generator occurred in 1947 when the RAND Corporation created a device that generated random numbers by attaching a roulette to a computer. Thanks to this device, scientists first accessed an extensive sequence of random numbers. They later published these sequences of numbers in a book intended for scientists to use in their experiments.

Another similar machine, ERNIE, built in today's famous Bletchley Park in the 1940s, was used to generate random numbers in the British Premium Bond lottery. Later, a documentary film "The Importance of Being E.R.N.I.E." was made about this random number generator in order to dispel suspicions about the dishonesty and non-randomness of its principle of operation.

John von Neumann further developed the random number generator in 1955. He created the "middle-square method," a process of generating random numbers used in simulation and modeling.

His idea was to start with some number, take its square, discard the digits from the middle of the result. Take the square again and discard the middle, and so on. In his opinion, the resulting sequence had the same properties as random numbers. Von Neumann's theory was not the optimal one. No matter what initial number you chose, the series generated this way would degenerate into a short cycle of repeating values like 8100, 6100, 4100, 8100, 6100, 4100.

Some computer programming languages still use John von Neumann's method.

In 1999, Intel added a hardware random number generator to the i810 chipset. This implementation gave truly random numbers based on temperature noise. Still, it did not work as fast as software random number generators. In 2012, Intel added RDRAND and RDSEED instructions to its chips to produce truly random numbers based on the same temperature fluctuations, but now at speeds of up to 500 Mb/s.

People are still debating which random number generator should be used in this or that system, operating system kernel, programming language, cryptographic library, etc. Many variants of algorithms are optimized for speed, memory saving, and security. Random number generators have evolved and are used in various applications, like creating random passwords, generating secure encryption keys, and simulating real-world events for research purposes.