Information For Readers

BJEDIS (Brazilian Journal of Experimental Design, Data Analysis and Inferential Statistics) is a peer-reviewed, open access journal dedicated to advancing the methodological, theoretical, and applied aspects of experimental design, statistical modeling, and data analysis across diverse fields. The journal places particular emphasis on rigorously grounded methodologies that support decision-making in complex, data-constrained, and uncertainty-rich environments. We welcome readers from academic, professional, and interdisciplinary backgrounds who are interested in the responsible use of statistics and artificial intelligence to support research, innovation, evidence-based reasoning, and decision-making.

All articles are freely available immediately upon publication. Readers can access cutting-edge research on topics such as experimental planning, regression analysis, multivariate methods, Bayesian inference, machine learning, data visualization, decision science under uncertainty, and statistical applications in science, engineering, social sciences, health, public policy, and education.

BJEDIS promotes transparency, reproducibility, and educational accessibility by publishing detailed methodological appendices, datasets when available, and tutorials aimed at bridging the gap between statistical theory and real-world practice. The journal also values approaches that explicitly address uncertainty, explainability, and ethical constraints, including the use of synthetic or privacy-preserving data when appropriate.

We encourage readers to engage with the content, cite the research, share articles with their communities, and contribute to the advancement of open and inclusive science that meaningfully connects statistical innovation with real-world impact.