True Random Number Generation of Very High Goodness-of-fit and Randomness Qualities

Abstract

The statistical nature of numerous problems in mathematics, physics and engineering have led to the development of methods for generating random data for a given distribution. Ancient methods include; dice, coin flipping and shuffling of cards. Today, various pseudo, quasi and true random generators ( RNGs) are being proposed for their improved properties. In this work, test metrics for goodness-of-fit and randomness are reviewed. The method of uniform sampling ( MUS) is modified for improving the randomness without harming the goodness-of-fit qualities. The test results illustrate that very high goodness-of-fit can be obtained even when the number of observed samples is as small as 10.