Statistical Training in Postgraduate Education: Challenges, Diagnosis, and Problems; Basis for an Innovative Pedagogical Proposal

Abstract

The teaching of statistics in postgraduate education faces crucial challenges, as its mastery is decisive for research quality across diverse disciplines. This study diagnosed the underlying causes hindering the rigorous use of statistical techniques in educational research, revealing errors in study design, inappropriate method selection, and failures in result interpretation. Furthermore, problems linked to insufficient training, the lack of advanced instruction, and the heterogeneity in researchers' mathematical preparation were identified. A quantitative, descriptive, and applied methodological design was adopted, combining empirical methods including observation, document review, and surveys. Data were processed using Excel pivot tables, statistical analysis (UCINET 6.804) for semantic networks, correlation and concordance tests (SPSS v.27), complemented by trend analysis via Google Trends, the Delphi method, and Logical Framework Analysis. The results obtained highlight the most relevant difficulties and the problems detected in the use of statistics. These findings serve as the starting point for formulating an innovative pedagogical proposal based on the Flipped Classroom model. This proposal aims to strengthen the development of statistical competencies and enhance the coherence of scientific research.