INTEGRATION OF ARTIFICIAL INTELLIGENCE AND INFERENTIAL ANALYSIS NODE GOOGLE COLLAB: APPLICATIONS IN QUANTITATIVE RESEARCH

This research addresses the use of Google Colab as a platform to perform parametric inferential statistical analyses integrated with artificial intelligence (AI) techniques. The increasing volume of data available for scientific studies demands tools that are capable of performing complex analyses efficiently and affordably. The central problem investigated was how Google Colab can optimize the execution of parametric inferential tests, combining them with AI methods, in order to generate robust and replicable results. The general objective was to demonstrate the applicability of this integration to improve the accuracy of statistical analyses. The research adopts the Giftedean neoperspectivist paradigm, using the theories of Complex Systems, Rational Action and Explainable Artificial Intelligence (XAI), and applies the hypothetical-deductive method. The analyses were performed through computer simulation and Python programming, using techniques such as linear regression, ANOVA, t-test, principal component analysis and machine learning. Among the main findings, we highlight the efficiency of Colab in processing large volumes of data and the potential of integrated techniques to broaden the understanding of the phenomena studied. To the gaps identified include the connectivity dependence and the need for more robust validation methods. The research contributes theoretically by integrating classical and modern methods, methodologically by presenting a replicable framework, and empirically by applying the results in different areas. It adds value by offering a practical guide for researchers and postgraduate students.