Tanyer, Suleyman Gokhun2024-03-202024-03-202014http://hdl.handle.net/11727/11901The 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.enginfo:eu-repo/semantics/closedAccessrandom number generatorstatistical signal processingprobabilitytest statisticsconferenceObject2132150003804462000352-s2.0-84949927853