The Importance of the Scientific Method in Machine Learning Education

Data science is maturing as an academic field, but many machine learning (ML) courses omit a core best practice from established STEM disciplines: the scientific method. This paper argues that centering rigorous, hypothesis-driven experimentation within ML education is essential for developing students’ critical thinking and analytical skills, especially in the age of AI. Drawing on the data science project life cycle the authors examine perspectives ranging from “problem-first” to “data-first” approaches, connecting them to ongoing debates about research replicability and variability in many analyst studies. A review of top-enrollment online ML courses and syllabi from top-100 U.S. universities reveals minimal explicit reference to scientific method concepts, data science life cycle frameworks, or structured experimental design. To address this gap, the authors offer concrete classroom examples, active learning, randomization, and A/B testing illustrating how scientific reasoning can be embedded into both model development and deployment phases. They conclude with recommendations for instructors: requiring hypothesis-driven analysis plans, incorporating randomization exercises, and using collaborative critique to reinforce experimental thinking. Embedding these practices, the authors contend, will produce more rigorous, ethical, and effective data science practitioners.