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<p>This study presents a novel approach for estimating the population mean by combining Simple Random Sampling (SRS) and Systematic Sampling within the framework of Stratified Sampling. While traditional stratified sampling typically employs random sampling techniques for each stratum, the integration of systematic sampling is proposed as an alternative for improved efficiency. This method enhances accuracy, particularly in heterogeneous populations, where stratification ensures subgroups are well-represented. The performance of SRS without replacement and Systematic Sampling under stratified conditions is compared in terms of bias and mean squared error. Through simulation studies, this new method demonstrates potential advantages over conventional stratified sampling methods, particularly regarding ease of implementation and robust estimation accuracy. This research contributes to sampling theory and offers practical guidance for statisticians seeking efficient strategies in complex population structures.</p>

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Estimating Population Mean: A New Approach Using Simple Random Sampling and Systematic Sampling within Stratified Sampling Framework

  • 期刊论文

  • 发表日期：

    2026年5月6日

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  • Sunil Kumar,
  • Ankush Kumar,
  • Chanda Rani

摘要

This study presents a novel approach for estimating the population mean by combining Simple Random Sampling (SRS) and Systematic Sampling within the framework of Stratified Sampling. While traditional stratified sampling typically employs random sampling techniques for each stratum, the integration of systematic sampling is proposed as an alternative for improved efficiency. This method enhances accuracy, particularly in heterogeneous populations, where stratification ensures subgroups are well-represented. The performance of SRS without replacement and Systematic Sampling under stratified conditions is compared in terms of bias and mean squared error. Through simulation studies, this new method demonstrates potential advantages over conventional stratified sampling methods, particularly regarding ease of implementation and robust estimation accuracy. This research contributes to sampling theory and offers practical guidance for statisticians seeking efficient strategies in complex population structures.