Transactions on Data Analysis in Social Science

Transactions on Data Analysis in Social Science

Machine Minds vs. Statistical Traditions: A Comparative Study in Humanities Data Analysis

Document Type : Original Article

Authors
M. Ghaem Tajgardoon 1
Z. Barzegar 2
1 Ministry of Science, Research and Technology, Tehran, Iran
2 Advanced Technologies in Medicines Dept., Iran University of Medical Science
10.22034/tdas.2025.245585
Abstract
The rapid growth in the volume, variety and complexity of digital data has changed how research in the humanities is designed and analyzed. Much of these data are unstructured and heterogeneous, which challenges traditional statistical tools and has encouraged the adoption of machine learning (ML). This article critically reviews and compares the two paradigms as applied in humanities research. ML can identify patterns in large and unstructured datasets without predefined models, but its limited interpretability raises concerns about the transparency, reliability and accountability of findings, which are central values in the humanities. Traditional statistical methods, grounded in hypothesis testing and statistical inference, offer transparency and well-established criteria for evaluating evidence, but they are less suited to complex, high-dimensional and unstructured data. The review compares the two approaches with respect to data characteristics, research aims, interpretability and the ethical implications of analytical choices. It argues that the choice between them should be driven by the research question and the nature of the data rather than by methodological trends, and that combined strategies, such as interpretable ML or ML-based exploration followed by statistical inference, can draw on the strengths of both. The article offers practical guidance for humanities researchers in selecting analytical methods.
Keywords
Big Data Analysis
Machine Learning
Traditional Statistics
Humanities Researches.
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Transactions on Data Analysis in Social Science
Volume 7, Issue 2
Spring 2025
Pages 110-122

  • Receive Date 10 January 2025
  • Revise Date 29 March 2025
  • Accept Date 17 May 2025