Statistical Hypothesis Testing in the Age of Artificial Intelligence

Beschreibung

This analysis critically examines the evolving role of statistical hypothesis testing in the age of AI, presenting detailed arguments for and against its use.
The increasing dominance of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning in data-driven disciplines has raised critical questions about the continuing relevance of traditional statistical hypothesis testing (SHT). While some scholars argue that the SHT paradigm has become obsolete in the era of predictive modeling, others highlight its continued utility for causal inference, robustness checks, and interpretability. This paper critically examines the evolving role of SHT in the age of AI, presenting detailed arguments for and against its use. The aim is to foster a nuanced understanding of when, why, and how SHT remains appropriate in the modern data science landscape.

Autor*in

Johann Markus Schauerhuber
During his studies and academic resp. professional activities Prof. Dr. Dr. Johann Markus Schauerhuber has been intensively involved in statistical programming, stochastic, risk theory, simulation and mathematical modeling. Most of his professional experience has been gained in the university domain as an academic director, postdoc lecturer / researcher and in government authorities. Email: jm_schauerhuber@gmx.at

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