Chong Ho Yu recently published two works that underscore his expertise and leadership in the evolving landscape of psychological research and statistical theory.
HPU Professor and Program Director of Data Science and Artificial Intelligence Chong Ho “Alex” Yu, Ph.D., continues to make waves at the intersection of data science and the social sciences with two newly published works that underscore his expertise and leadership in the evolving landscape of psychological research and statistical theory.
Yu recently served as editor-in-chief for a special issue of Frontiers in Psychology. His editorial, titled “Data Science and Machine Learning for Psychological Research,” outlines a critical shift underway in the field.
Chong Ho Yu.
“It would be incorrect to declare classical statistics obsolete,” Yu writes. “However, the studies here clearly illustrate that psychology and the broader social sciences have entered a paradigm shift. Data science and machine learning are no longer optional complements; they are essential pillars of contemporary research methodology.”
He emphasizes that while foundational tools like p-values and hypothesis testing still serve a role, researchers must embrace advanced analytical frameworks to stay relevant and generate meaningful insights in today’s increasingly complex, data-driven world. The editorial is available online at Frontiers in Psychology.
In addition, Yu authored and co-authored two chapters in the newly published second edition of the International Encyclopedia of Statistical Sciences (Springer, 2025). In “Data Science, Data Mining, Machine Learning, Big Data and Statistics,” co-written with editor Miodrag Lovric, Yu examines the conceptual and practical distinctions among key analytical domains while highlighting their growing interconnectivity. The chapter defines data science as an interdisciplinary field grounded in statistics, machine learning, and domain expertise, exploring how it diverges from, yet depends upon, traditional methods.
In the second chapter authored by Yu, “Degrees of Freedom,” Yu reframes a fundamental concept in classical statistics through a contemporary lens. While degrees of freedom remain central to many traditional analyses, he argues that their practical significance is diminishing in modern data science and machine learning workflows, where such constraints are less relevant and rarely reported.
The International Encyclopedia of Statistical Sciences (2nd ed.) is available now through Amazon.
A three-time recipient of the SAS Faculty Scholarship and the Distinguished SAS Educator Award, Yu brings a wealth of insight to his work at the intersection of data, ethics, and education. His research spans data science methodology, cross-cultural analysis, and the ethical implications of emerging technologies, placing him at the forefront of global conversations on artificial intelligence, fairness, and the future of learning.
“As researchers, we’re not just crunching numbers. We’re always seeking meaning, fairness, and insight in a world that’s rapidly changing. Data science gives us the tools, but it’s our responsibility to ask the right questions,” said Yu.
