The future of statistics in scientific research: a narrative review of emerging trends
DOI:
https://doi.org/10.66778/SI.v04n02.07Keywords:
biomedical research, artificial intelligence, big data, machine learningAbstract
BACKGROUND: The rapid expansion of big data, artificial intelligence, and data science has substantially altered the role of statistics in scientific research. Traditionally considered the methodological pillar of the scientific method, statistics has provided the fundamental tools for experimental design, data analysis, and inference under uncertainty. However, the development of advanced computational techniques has reshaped the analytical landscape, creating new opportunities and challenges. OBJECTIVE: This study aims to analyze the emerging trends that are redefining the role of statistics in contemporary scientific research. METHODOLOGY: To this end, a qualitative approach was adopted based on a narrative review of scientific literature published between 2020 and 2025. After applying inclusion and exclusion criteria across various academic databases, 20 relevant studies were selected for conceptual analysis. RESULTS: The findings reveal four major trends: 1) the transition toward hybrid methodological frameworks that integrate statistical inference with machine learning; 2) the urgent need to develop interpretable predictive models; 3) the strengthening of open science practices to combat the reproducibility crisis; and 4) the transformation of statistical education toward computational competencies. IN CONCLUSION, the review suggests that statistics is not being displaced but is rather evolving in an interdisciplinary manner to continue playing a central role in model validation, uncertainty management, and the generation of reliable scientific knowledge.
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