Citation: Zakari, Y.; Muhammad, I. Modified Estimator of Finite Population Variance under Stratified Random Sampling. Eng. Proc. 2023, 56, 177. https://doi.org/ 10.3390/ASEC2023-16308 Academic Editor: Santosh Kumar Published: 21 November 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Proceeding Paper Modified Estimator of Finite Population Variance under Stratified Random Sampling † Yahaya Zakari 1, * and Isah Muhammad 2 1 Department of Statistics, Ahmadu Bello University, Zaria 810107, Nigeria 2 Department of Statistics, Binyaminu Usman Polytechnic, Hadejia 731101, Nigeria; isahsta@gmail.com * Correspondence: yzakari@abu.edu.ng † Presented at the 4th International Electronic Conference on Applied Sciences, 27 October–10 November 2023; Available online: https://asec2023.sciforum.net/. Abstract: This paper proposes a generalized estimator of finite population variance using the auxiliary information under stratified random sampling. The expressions for bias and mean square error equations of the proposed estimator up to the first degree of approximation are derived. The theoretical efficiency conditions under which the proposed estimator is better than some existing estimators are obtained. The performances of the existing and proposed estimators were assessed using three real datasets based on the criteria of minimum mean square error and supreme percentage relative efficiency. Evidence from the study showed that the proposed estimator performed better and was more efficient than some existing estimators considered. Keywords: auxiliary variable; mean square error; bias; efficiency 1. Introduction Researchers have been considering the use of auxiliary information in various forms to construct more accurate estimators for population parameters in experimental surveys. This approach has been shown to significantly improve the accuracy of the population mean estimation, and it has been explored by several researchers, including [1–3]. The field of survey sampling has seen a significant amount of research devoted to developing estimators of population variance to measure variations that exist in real-world scenarios. These variations can arise in various industries, such as manufacturing, pharmaceuticals, agriculture, and biological experiments. Researchers like [4–14] have worked extensively in this area. In real-life situations, population units of variables of interest can have diverse features, which makes the population units heterogeneous. In such cases, estimators of finite population variance, based on simple random sampling, cannot be applied. Therefore, the stratification approach is used to provide precise results when population units are heterogeneous [5,15–17]. Most ratio-based and product-based estimators are not efficient in estimating popula- tion characteristics when there is a negative correlation between the study and auxiliary variables for ratio-based estimators or a positive correlation for product-based estimators. To address this issue under stratified random sampling, a new generalized ratio-product cum regression-type estimator is proposed. The generalized ratio-product cum regression- type estimator proposed in this study is flexible in providing better efficient estimates than existing estimators when the data exhibits either a negative or positive correlation between the study and auxiliary variables. Following the introduction is Section 2, which contains the notations and the literature review, while Section 3 presents the methodology of the study. Section 4 presents the efficiency conditions of the proposed methodology. Section 5 discusses the results while the conclusion and recommendations are presented in Section 6. Eng. Proc. 2023, 56, 177. https://doi.org/10.3390/ASEC2023-16308 https://www.mdpi.com/journal/engproc