ArticleCureus2026
SPSS 31.0 Versus GPT-5.5: Performance Across Core Epidemiological and Non-parametric Statistics in Medicine and Applied Health Sciences.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background The growing adoption of artificial intelligence (AI) technologies, including large language models (LLMs), such as ChatGPT (OpenAI, San Francisco, CA) and many others, has expanded their role in statistical analysis and scientific research workflows. Despite this increasing use, conventional statistical software, such as IBM SPSS (IBM Corp., Armonk, NY), remains the accepted reference standard because of its established methodological precision, transparency, and reproducibility. Consequently, it is important to determine whether statistical results generated by LLMs achieve comparable levels of accuracy and consistency. As these systems undergo continual refinement, independent replication, comparative validation, and continuation studies are necessary to assess whether newer model generations produce findings that reliably correspond with those obtained using established statistical methodologies. Methodology Thirteen statistical procedures commonly used in clinical, medical, epidemiological, and applied health sciences research were evaluated using authentic datasets derived from previously published peer-reviewed studies and undergraduate applied health sciences coursework. Analyses included epidemiological tests, nonparametric analyses, chi-square analyses, univariable and multivariable logistic regression, Kaplan-Meier survival analysis, Cox proportional hazards regression, and measures of nominal association. For each procedure, dataset and variable names were copied directly from SPSS 31.0 data files and analyzed in GPT-5.5 using standardized prompts. AI-generated results were then compared directly with SPSS 31.0 outputs to evaluate computational agreement, methodological consistency, and inferential concordance across all statistical procedures. Results GPT-5.5 demonstrated a high degree of agreement with SPSS 31.0 across all statistical procedures evaluated, including risk and odds ratios, Wilcoxon signed-rank, Mann-Whitney U, Kruskal-Wallis, Friedman's test, one-way and two-way chi-square, binary and multivariable logistic regression, Kaplan-Meier survival analysis, Cox proportional hazards model, and phi and Cramer's V. Most analyses produced identical test statistics, effect estimates, confidence intervals, and inferential conclusions. Minor numerical differences were observed only in selected approximation statistics, including standardized
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.