A new statistical method with properties: Analyzing the impact of digitalization on vocational education and radiation data

The main aim of the paper is to suggest a new estimator of the population mean with the help of auxiliary information based on the empirical distribution function especially with complex and heterogeneous data produced by digitalized environments. The rapid process of digitalization of the vocational education system and the growth of the use of radiation monitoring technologies have created non homogeneous large mass data in which traditional mean estimators might not be efficient. In order to overcome this problem, a new statistical estimator is suggested. Theoretical characteristics of the estimator such as bias and mean squared error (MSE) are obtained and analytically discussed. Comparative study with existing estimators shows that the proposed estimator has a lower MSE value in standard conditions and therefore is more efficient. The estimator performance is also checked by relying on real-world datasets in the fields of vocational education and radiation. The empirical evidence proves that the suggested method is more stable and efficient than the traditional ones. In general, the results indicate that the proposed estimator is a useful and consistent mechanism to estimate the population mean when data is large and there is a lot of uncertainty. This work would be added to estimation theory in the sense that it would provide a means of combining robust theoretical characteristics and practical usability, and it is likely to have an impact on researchers and practitioners in the area of education analytics, radiation research, and others which are subject to the digital transformation.