Random number generator

This random number generator can generate random numbers within specified ranges. You can set the lower and upper limits, choose whether to generate integers or decimals, specify the precision of decimals, determine the number of random numbers to generate, allow or disallow repeats, and choose to sort the generated numbers in ascending or descending order.

To use the random number generator:

1. Choose whether to generate integers or decimals.
2. Enter the lower and upper limits for the range of random numbers.
3. If generating decimals, specify the desired precision. The generator can can deal with numbers up to 35 digits of precision.
4. Enter the number of random numbers to generate (up to 10,000 numbers).
5. Choose whether to allow repeats or not.
6. Optionally, select the sorting order for the generated numbers.
7. Click the “Generate” button to generate the random numbers.
8. Optionally, click the “Copy to Clipboard” button to copy the generated numbers.

The output will display the randomly generated numbers based on the specified parameters. You can adjust the input parameters as needed and generate new sets of random numbers accordingly.

Related calculators:

What is a random number?

A random number is a number chosen from a set or range unpredictably, without any discernible pattern. It’s often generated by a random number generator, which uses a seed value or algorithm to produce seemingly unpredictable sequences of numbers. These numbers are frequently used in various fields like cryptography, gaming, statistics, and simulations where unpredictability or randomness is desirable.

Methods to generate random numbers

1. Pseudorandom number generators (PRNGs): These algorithms generate sequences of numbers that appear random but are actually determined by an initial value called a seed. PRNGs use mathematical formulas to produce sequences that have properties of randomness. Common PRNG algorithms include Linear Congruential Generators (LCGs), Mersenne Twister, and XORshift.
2. Hardware random number generators (HRNGs): These generators use physical processes to generate randomness. They rely on unpredictable physical phenomena such as electronic noise, radioactive decay, or thermal noise. HRNGs provide true randomness and are often used in applications where security is critical, like cryptography.
3. Combining sources: Some systems combine outputs from both PRNGs and HRNGs to balance efficiency and randomness. This approach, known as entropy harvesting, ensures a high level of unpredictability while still maintaining speed and efficiency.
4. Cryptographically secure PRNGs (CSPRNGs): These generators produce random numbers suitable for use in cryptographic applications. They have properties that make it practically impossible to predict their output without knowledge of the seed. CSPRNGs typically combine elements of both PRNGs and HRNGs to ensure high-quality randomness.

In summary, random numbers can be generated using mathematical algorithms, physical processes, or a combination of both, depending on the application’s requirements for randomness, speed, and security.

Are random numbers generated by computers truly random?

Random numbers generated by computers are not truly random in the strictest sense because they are ultimately deterministic, meaning they are produced by algorithms. However, the quality of randomness depends on the method used for generation.

1. Pseudorandom number generators (PRNGs): PRNGs produce sequences of numbers that appear random but are deterministic, meaning the sequence can be reproduced if you know the initial seed value. While these sequences may exhibit statistical randomness and be suitable for many applications, they are not truly random.
2. Hardware random number generators (HRNGs): HRNGs rely on physical processes like electronic noise or radioactive decay to generate randomness. The output of HRNGs is considered truly random as it’s based on inherently unpredictable physical phenomena.
3. Cryptographically secure PRNGs (CSPRNGs): CSPRNGs are designed to produce sequences of numbers that are indistinguishable from true randomness, even for someone with knowledge of the algorithm and some portion of the sequence. They typically combine elements of PRNGs and HRNGs to achieve this high level of randomness.

Overall, while the random numbers generated by computers are not truly random in the purest sense, they can exhibit properties of randomness depending on the method used. For many practical purposes, such as simulations, gaming, and statistical analysis, pseudorandom numbers suffice. However, for applications requiring high security or cryptographic purposes, true randomness from hardware sources or cryptographically secure algorithms is essential.