Efficient classes of estimators for estimating indeterminate population mean using neutrosophic ranked set sampling

Estimating the population mean with accuracy is frequently challenged by uncertainty and inaccurate data in survey sampling. This study presents some efficient classes of estimators for estimating the indeterminate population mean using neutrosophic ranked set sampling (NRSS). The study establishes the bias and mean squared error (MSE) of the suggested estimators and compares their performance with the existing neutrosophic estimators. Analytical comparisons show considerable efficiency benefits over the existing neutrosophic estimators. Simulation research and executions on real-life datasets confirm the accuracy of the proposed neutrosophic estimators when dealing with uncertainty. The findings highlight the potential of proposed NRSS estimators as a powerful tool for population mean estimation by providing new insights into statistical methodologies for uncertain and imprecise situations.