Random Number Generator
Random = Math.floor(Math.random() * (max - min + 1)) + min
Example:
Math.floor(Math.random() * (100 - 1 + 1)) + 1 = Math.floor(Math.random() * 100) + 1 = e.g., 42
A random number is a value selected unpredictably from a set range, with no recognizable pattern. These values are usually independent of each other, although in some cases, the distribution they follow may affect how they appear. For example, student heights in a school typically follow a normal distribution — most fall near the average, while very tall or very short individuals are less common. When you randomly pick a student’s height, it’s more likely to be close to the average than at the extremes.
The random number generators available here are designed to produce values that are statistically independent and uniformly distributed across the specified range.
A random number generator (RNG) is a tool or system that outputs random values within a defined range. RNGs can be classified into two main types: hardware-based and software-based (also known as pseudo-random number generators). Physical RNGs may use tangible tools like dice rolls, coin flips, or other mechanical means to produce randomness.
Pseudo-random number generators (PRNGs), on the other hand, are algorithms used to simulate random behavior. While these numbers are generated using mathematical formulas, they closely mimic true randomness and are suitable for most everyday uses. Most computer-generated random numbers—including those from the tool on this page—fall into this category. However, since they rely on algorithms, they are not truly random and should not be used in sensitive fields like cryptography.
For applications requiring higher security or absolute randomness, values derived from natural phenomena—such as atmospheric noise or quantum fluctuations—are preferred. These methods also include adjustments to eliminate any bias that might come from how the data is collected.