No historical records yet
The generated random number will be displayed here
Random numbers are values drawn with equal probability within a certain range and are widely used in computer science, statistics, cryptography and other fields.
Influencing factors: random number generation algorithm, entropy source quality, random seed selection, etc.
Random number generation involves multiple scientific fields, including mathematics, physics, and computer science:
In computers, most random numbers are actually "pseudo-random numbers", which are generated by deterministic algorithms but exhibit statistically randomness. True random numbers need to take advantage of uncertainties in physical processes, such as radioactive decay or atmospheric noise.
The quality of random numbers is often evaluated by a variety of statistical tests, such as the NIST Random Number Test Suite, which detects patterns and deviations in a sequence.
True random numbers are generated based on uncertainty in physical processes, such as quantum phenomena or thermal noise. They are unpredictable and unreproducible, unlike pseudo-random numbers generated based on algorithms.
The Math.random() function used by the browser generates pseudo-random numbers, which are suitable for general purposes but are not suitable for cryptography applications. For higher security needs, modern browsers provide the getRandomValues() method in the Web Crypto API.
The quality of random numbers can be evaluated through a variety of statistical tests, such as frequency tests, run tests, and entropy tests. Professional tools include NIST Random Number Test Suite and Diehard Testing.
Random seeds are the initial value of the pseudo-random number generator, which determines the entire random sequence that is subsequently generated. Setting up fixed seeds can make random sequences reproducible, which is useful in scientific experiments and debugging.
Set a suitable range of random numbers according to specific needs to avoid excessive or too small range affecting the use effect.
For security-sensitive scenarios, use a cryptographically secure random number generator to avoid simple pseudo-random numbers.
For important random results, save or export them in time to avoid data loss due to refreshing pages or clearing caches.
Use data visualization tools to analyze the random number distribution to ensure that the generated results meet the expected randomness requirements.
Pseudo-random numbers are seemingly random but actually predictable sequences of numbers generated by deterministic algorithms. Most "random numbers" in computers are actually pseudo-random numbers, which are based on an initial seed value and can generate sequences with good statistical characteristics.
Entropy is an important indicator for measuring stochasticity. High entropy values indicate higher uncertainty and randomness. The true random number generator captures entropy from physical phenomena, while the pseudo-random number generator relies on algorithmic design to simulate high entropy characteristics.
All pseudo-random number generators have a "cycle", after which the sequence will start to repeat. Good random number generators have very long periods, such as the Mason rotation algorithm (MT19937) has a period of 2^19937-1, which is huge enough to avoid duplication in practical applications.
Basic random numbers are usually evenly distributed, with the probability of occurrence of each number equal. In some applications, non-uniformly distributed random numbers are needed, such as normal distribution (Gaussian distribution), exponential distribution, etc., which can be obtained from uniformly distributed random numbers through specific algorithms.