Uncertainty based new class of neutrosophic estimators of population variance using simple random sampling

In sampling surveys, estimating the population variance accurately is important for data analysis and decision-making. The conventional estimators often fail to account for uncertainty, leading to biased or inefficient results. This article proposes an uncertainty-based new class of neutrosophic estimators for the population variance using simple random sampling (SRS). The proposed neutrosophic estimators use the auxiliary information to enhance the efficiency of the estimates. The bias and mean square error (MSE) expressions for the proposed neutrosophic estimators are derived and compared their performance with the adapted neutrosophic estimators. Simulation studies and real data applications show that the proposed neutrosophic estimators obtain lower MSE and higher percent relative efficiency (PRE) under different conditions. The findings suggest that the proposed estimators significantly improve the precision of the estimates under uncertain environments.