Currently, statistical tests for random number generators (RNGs) are widely used in practice, and some of them are even included in information security standards. But despite the popularity of RNGs, consistent tests are known only for stationary ergodic deviations of randomness (a test is consistent if it detects any deviations from a given class when the sample size goes to $ \infty $). However, the model of a stationary ergodic source is too narrow for some RNGs, in particular, for generators based on physical effects. In this article, we propose computable consistent tests for some classes of deviations more general than stationary ergodic and describe some general properties of statistical tests. The proposed approach and the resulting test are based on the ideas and methods of information theory.

翻译：目前,随机数字发电机(RNGs)的统计测试在实践中广泛使用,其中一些甚至被纳入信息安全标准,但尽管RNGs很受欢迎,但人们只知道随机性固定偏差(如果它检测到某一类的偏差,样本大小达到$=美元时,这种测试是一致的);然而,对于一些RNGs来说,固定ERGidic源的模式过于狭窄,特别是对于基于物理效应的发电机来说,我们建议对某些类别的偏差进行比固定偏差更一般的比较一致的测试,并描述统计测试的一些一般特性,提议的方法和由此产生的测试基于信息理论的想法和方法。