Reshaping Statistical Analysis Using Generative Artificial Intelligence: A Comparative Study Between the Claud Tool and SPSS Software

Abstract

This study aimed to evaluate the capability of Claude, a generative artificial intelligence tool, in performing descriptive and inferential statistical analyses and to compare its performance with SPSS, one of the most widely used statistical software packages in scientific research. The study adopted a comparative experimental approach by applying a unified set of statistical procedures to a simulated dataset consisting of 50 observations designed to resemble real-world workplace data. The analyses included descriptive statistics, independent-samples t-tests, one-way ANOVA, multiple regression analysis, and Pearson correlation coefficients. The findings revealed that SPSS achieved perfect computational accuracy (100%), whereas Claude demonstrated high levels of accuracy, reaching 98.2% in descriptive analysis and 97.4% in inferential analysis. Claude also outperformed SPSS in terms of processing speed, ease of use, and the quality of statistical interpretation. Furthermore, the results indicated that Claude provides rich contextual explanations that enhance researchers’ understanding of statistical findings beyond conventional numerical outputs. The study recommends adopting an integrative approach that combines the computational precision of specialized statistical software with the interpretive capabilities of generative AI tools, while establishing academic guidelines to verify, document, and regulate the use of AI-generated analytical outputs in scientific research.