# An efficient method of generation of a random sample using random numbers significantly less than the sample size

Author Affiliations

^{1}Department of Computer Science and Engineering, BIT Mesra, Ranchi-835215, India^{2}Department of Mathematics, BIT Mesra, Ranchi-835215, India

*Res. J. Mathematical & Statistical Sci.,* **Volume 7, Issue (2),** Pages 27-29, May,12 **(2019)**

## Abstract

Given that the pseudo-random numbers generated by the computer have a cycle; it is wise not to lose random numbers in simulation studies. For drawing a random sample of size n from a population of size N (n<=N), the existing sampling algorithms require n pseudo-random numbers. If N is large, accordingly n should also be large for better representation of the population. Since most simulation studies require at least 500 samples, we would need 500xn pseudo random numbers which can lead to cycle break. We are therefore motivated to develop an efficient sampling algorithm which generates the desired sample using random numbers significantly less than the sample size. Our algorithm has the facility that a single pseudo-random number can generate the sample of size 60 for a population of size 100000 using a python code. We would of course need more than one pseudo-random number if the sample size exceeds 60 for this population.

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