An Efficient Class of Estimators in Stratified Random Sampling with an Application to Real Data

This research article addresses an efficient separate and combined class of estimators for the population mean estimation based on stratified random sampling (StRS). The first order approximated expressions bias square error proposed are obtained. A comparative study is conducted to determine efficiency conditions in which suggested outperforms contemporary estimators. These examined through extensive simulation by employing a hypothetically drawn symmetrical asymmetrical populations. results have shown that more effective than other available In addition, application methods also presented examining real data set.