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Randomness tests
Randomness tests (or tests for randomness), in data evaluation, are used to analyze the distribution pattern of a set of data. In stochastic modeling, as in some computer simulations, the expected random input data can be verified, by a formal test for randomness, to show that the simulation runs were performed using randomized data. In some cases, data reveals an obvious non-random pattern, as with so-called "runs in the data" (such as expecting random 0–9 but finding "4 3 2 1 0 4 3 2 1..." and rarely going above 4). If a selected set of data fails the tests, then parameters can be changed or other randomized data can be used which does pass the tests for randomness.
There are many practical measures of randomness for a binary sequence. These include measures based on statistical tests, transforms, and complexity or a mixture of these.
== Background ==
The issue of randomness is an important philosophical and theoretical question. Tests for randomness can be used to determine whether a data set has a recognisable pattern, and therefore whether the process that generated it is significantly random.
Many random number generators in use today are prescribed algorithms, and so are actually pseudo-random number generators. The sequences they produce are called pseudo-random sequences. These generators do not always generate sequences which are sufficiently random and contains patterns. For example, the infamous RANDU fails many randomness tests dramatically, including the Spectral Test. Wolfram used randomness tests on the output of Rule 30 to examine its potential for generating random numbers, though it was shown to have an effective key size far smaller than its actual size and to perform poorly on a chi-squared test.〔.〕 The use of an ill-conceived random number generator can put the validity of an experiment in doubt by violating statistical assumptions. Though there are commonly used statistical testing techniques such as NIST standards, Yongge Wang showed that NIST standards are not sufficient. Furthermore, Yongge Wang designed statistical distance based and the law of the iterated logarithm based testing techniques. Using this technique, Yongge Wang and Tony Nicol detect the weakness in commonly used pseudorandom generators such as the well known Debian version of OpenSSL pseudorandom generator.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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