+A RandomNumberGenerator is either a piece of hardware or software that generates random numbers in the widest sense. A good one is crucial to applications in [Cryptography]: if an attacker can guess the sequence of "random" numbers used to generate keys or other variables for your algorithm, he gets an important foot in the door for cracking your communication -- or can even walk right in. There are many different approaches for [RandomNumberGenerator]s, however, the biggest distinction is between true vs pseudorandom number generators . 

+These __must__ be hardware by definition . A variety of approaches for their design exist, but listening to white noise or radioactive decay seem to be the most popular ones. There are a number of publicly queriable true random number generators on the web, such as [http://www.random.org/] and [http://www.fourmilab.ch/hotbits/]. 

+A pseudo random number generator such as offered by the random(3) and rand(3) functions of the standard [C] library generates numbers using mathematical [Algorithm]s . Such a generator has a "period", which means that it produces a repeating sequence of numbers. With a good algorithm, the period length can well reach billions of numbers. 

+However, designing such an algorithm is difficult terrain which requires thorough understanding of statistics and very sharp math skills. __Do not attempt to roll your own.__ Most naive attempts at handrolled pseudorandom number generators have alarmingly short period lengths. Often, hapless programmers will try to make a "better" generator by lumping together two different algorithms with decent period lengths each -- but the result is almost inevitably a generator with a ''shorter'' period! 

+Note that true randomness is not always desirable. Random number sequences often have certain specific characteristics which make them better suited as input to certain kinds of statistical algorithm, because they cause a far faster convergence toward the same final result than truly random numbers could. Truly random numbers also make debugging much harder for obvious reasons; if you want to verify an algorithm, you want to be able to replay "random" number sequences as necessary. On the other hand, you will usually need a statistically decently random sequence to examine the longterm behaviour of the algorithm, so just using a sequence with regular characteristics wouldn't work .