The article by Stephen K. Park and Keith W. Miller discusses the importance of random number generators in scientific computing and highlights the challenges in finding good, portable, industry-standard software for this purpose. Despite the widespread recognition of the significance of random number generation, many generators available in textbooks and software packages are unsatisfactory. The authors advocate the use of a minimal standard generator, specifically a multiplicative linear congruential generator with multiplier 16807 and prime modulus \(2^{31} - 1\), which has been extensively tested and is known to produce statistically independent random numbers. They provide detailed explanations of the generator's implementation in high-level languages and discuss theoretical considerations, including the tests for full period, randomness, and efficient implementation. The article also criticizes several inadequate generators found in recent computer science textbooks and programming environments, emphasizing the need for better random number generation practices.The article by Stephen K. Park and Keith W. Miller discusses the importance of random number generators in scientific computing and highlights the challenges in finding good, portable, industry-standard software for this purpose. Despite the widespread recognition of the significance of random number generation, many generators available in textbooks and software packages are unsatisfactory. The authors advocate the use of a minimal standard generator, specifically a multiplicative linear congruential generator with multiplier 16807 and prime modulus \(2^{31} - 1\), which has been extensively tested and is known to produce statistically independent random numbers. They provide detailed explanations of the generator's implementation in high-level languages and discuss theoretical considerations, including the tests for full period, randomness, and efficient implementation. The article also criticizes several inadequate generators found in recent computer science textbooks and programming environments, emphasizing the need for better random number generation practices.