Why Traditional Statistical Methods Need to Evolve in the Age of Artificial Intelligence: A Biostatistical Perspective

Abstract

Traditional statistical methods, basically the frequentist approach, must evolve to remain relevant in the age of Artificial Intelligence (AI). While Conventional statistical methods work under theoretical assumptions, they struggle to handle the complexities of modern biomedical data, including high dimensionality, non-linearity, and violations of key assumptions However, this is not a problem for the newer machine learning models like support vector machines. There are new techniques like regularization (ridge, lasso) to handle many of the assumptions in traditional statistical methods, which can be implemented and automated using software like R and Python.  Machine learning as a part of AI offers solutions by handling large-scale complex datasets, uncovering hidden patterns, and improving prediction power. They are based on the foundation models where statistics and mathematics meet. So, just talking about the limitations of the statistical methods is half true. The viewpoint tries to explain why to integrate AI with traditional biostatistics, creating hybrid models that combine statistical rigor with AI flexibility. Integration can enhance data analysis, causal inference, and decision-making, ultimately advancing personalized medicine and public health, ethically and transparently.