Scientific research methodology applied to artificial intelligence and data science - García Cruz, Josefina Arimatea (2025)
The history of science has gone through various paradigms: from the empirical observation of natural phenomena and the theoretical formulation of laws to the computational simulation of complex systems. Today, we are immersed in what Jim Gray called the "fourth paradigm": data-intensive scientific discovery. In this new scenario, Artificial Intelligence (AI) and Data Science are not mere technical engineering tools; they have become the fundamental lens through which we interrogate reality. However, the dizzying advance of these technologies has brought with it an epistemological challenge: the gap between predictive capacity and scientific validity. In the race to optimize hyperparameters and reduce mean square error, it is often forgotten that an AI model, in a research context, is not just a software product, but a mathematically formalized hypothesis that must be tested, disproved, and explained. There is a latent tension in current practice. On the one hand, software engineering seeks to make the system "work"; on the other, the methodology of research requires understanding "why it works" and under what conditions it is reproducible. This book, Scientific research methodology applied to artificial intelligence and data science: General approach, was born from the imperative need to systematize research in this field. It seeks to answer critical questions that many students and professionals face: How do you formulate a valid research question in a Machine Learning project?, What differentiates a technology implementation project from a scientific research thesis?, How do we address the reproducibility crisis in "Black Box" models?, What are the ethical and bias standards that should govern data collection?. The central objective of this text is to serve as a bridge that connects the rigor of the traditional scientific method—with its emphasis on hypothesis, controlled experimentation, and causal inference—with the flexibility and power of modern Data Science workflows (CRISP-DM, KDD, etc.). It is not a programming manual in Python or R, nor a compendium of neural network architectures. It is a guide to scientific thinking applied to data. Here, the reader will learn how to structure big data chaos within a methodological framework that ensures robust, generalizable, and ethically responsible findings. Through the four chapters, we will break down the AI research lifecycle from a methodological perspective: -Epistemological Foundations: We will explore the nature of knowledge generated by inductive and deductive algorithms. -Research Design: Definition of scope, selection of variables and the dichotomy between explanatory and predictive studies. -Data Management as Evidence: Processing, cleansing, and the importance of data quality beyond volume. -Validation and Metrics: Going beyond accuracy as the only metric; sensitivity analysis, robustness, and rigorous cross-validation. -Ethics and Communication: The Researcher's Responsibility in the Face of Algorithmic Bias and How to Write Technical Findings for a Scientific Audience. Ultimately, this book proposes that the best Artificial Intelligence is not the one that simply processes data faster, but the one that helps us better understand the world with greater rigor and truth. Welcome to the study of scientific methodology in the age of data.