SAMPLING TECHNIQUES & DETERMINATION OF SAMPLE SIZE IN APPLIED STATISTICS RESEARCH: AN OVERVIEW

TLDR

Applied statistics research relies heavily on sampling and sample size determination, especially in survey-based studies across social, agricultural, health, and business sciences, where inappropriate techniques or sizes can lead to erroneous conclusions. This paper provides an overview of key sampling concepts and sample size calculation terms, such as random sampling, stratified sampling, power, and confidence intervals. It also describes various sampling methods—including purposive, random, stratified, systematic, and quota sampling—for specific research purposes.

Abstract

Applied statistics research plays pivotal role in diverse problems of social sciences, agricultural sciences, health sciences, and business research. Many investigations are conducted by survey research. The technique of sampling and determination of sample size have crucial role in survey-based research problems in applied statistics. Specific sampling techniques are used for specific research problems because one technique may not be appropriate for all problems. Similarly, if the sample size is inappropriate it may lead to erroneous conclusions. The present paper gives an overview of some commonly used terms and techniques such as sample, random sampling, stratified random sampling, power of the test, confidence interval that need to be specified for a sample size calculation and some techniques for determination of sample size, and also describes some sampling methods such as purposive random sampling, random sampling, stratified random sampling, systematic random sampling and quota sampling for specific research purposes.