Applications of ChatGPT in Data Analysis and Interpretation

Abstract The integration of Large Language Models (LLMs) like ChatGPT into data science is rapidly evolving from a novel prototype to a functional, yet constrained, assistant within the analytical pipeline. This nano review critically examines its emergent role in augmenting—rather than automating—the interpretive and procedural dimensions of data work. The analysis details its utility in generating and explaining code for statistical procedures, translating natural language queries into executable commands, and drafting narrative summaries of analytical results. However, it foregrounds the model’s fundamental limitations as a stochastic text generator, including its incapacity for genuine mathematical reasoning, a high propensity for logical and coding “hallucinations,” and a lack of domain-specific contextual understanding. Consequently, the review argues that effective implementation requires a rigorous “human-in-the-loop” paradigm, where the data scientist’s expertise in methodology, validation, and ethical interpretation remains paramount. The future of AI-augmented analysis depends on leveraging LLMs for syntactic and communicative efficiency while steadfastly anchoring analytical rigor and insight in human judgment. Keywords: ChatGPT, large language models (LLMs), data analysis, data science, AI augmentation, computational social science, statistical analysis, human-in-the-loop, analytical pipeline