Random is a term used in mathematics, as well as in a more general use, for situations when there is no way to know the outcome (what will happen). Something that is chosen at random is not chosen for any special reason, but purely by chance. An example of a random act is a lottery.
A computer can make lists of random numbers. Humans are unable to do this, because the brain works in patterns. If someone is asked to keep saying "heads" or "tails" at random, a computer would soon be able to tell which one the person is likely to say next because the computer notices the patterns.
Sometimes the word "random" is used more loosely. There are websites of random jokes, which just means: a variety of jokes about all sorts of things.
In recent years young people have started to use the word "random" even more loosely to describe anything which is rather strange or has no logic. Sentences such as "the mouldy cheese is escaping" or "I like pie and spam" might be described as "random", although this is not the proper dictionary or mathematical meaning of the word.
Making random numbers [change]
There are several ways in which a process or system can be seen as random:
- Randomness coming from the environment (for example, Brownian motion, but also hardware random number generators)
- Randomness coming from the starting conditions. This aspect is studied by chaos theory. It can be observed in systems that depend very much on differences in the conditions at the start. Examples of such systems are pachinko or dice.
- Randomness generated by the system itself. This is also called pseudorandomness, and is the kind used in pseudo-random number generators. There are many algorithms (based on arithmetics or cellular automata) to generate pseudorandom numbers. The behavior of such a system can be predicted, if the random seed and the algorithm are known. These methods are quicker than getting "true" randomness from the environment.
The many applications of randomness have led to many different methods for generating random data. These methods may vary as to how unpredictable or statistically random they are, and how quickly they can generate random numbers.
Before there were computational random number generators, generating large amounts of sufficiently random numbers (important in statistics) required a lot of work. Results would sometimes be collected and distributed as random number tables.
Computers can make what appear to be random numbers. There are two ways to make them.
- There are different algorithms to make random numbers. This makes it possible to model certain aspects of randomness, for example the distribution of the generated numbers. Numbers generated in such a fashion will however always follow a pattern. Given one, or a few of them, a computer can calculate the next random number. Therefore, such numbers are called pseudorandom.
- True random numbers are generated by observing a non-deterministic experiment. The number is then calculated based on the result of the experiment. An example might be to hook up a Geiger counter to a computer, to generate the number.
Other pages [change]