ArticleCureus2024
A Comparative Evaluation of Statistical Product and Service Solutions (SPSS) and ChatGPT-4 in Statistical Analyses.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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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.
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Who cites it
6 citing papers in PubMed.
- Statistical Consistency in Artificial Intelligence-Assisted Computations: A Comparison of SPSS 31.0 and GPT-5.5.Cureus · 2026Article
- Psychosocial and Social Security Risks Linked to Vaccine Misinformation in Romania: Implications for Vaccination Acceptance and Public Policy.Behavioral sciences (Basel, Switzerland) · 2026Article
- Applications of Large Language Models in Medical Research: From Systematic Reviews to Clinical Studies.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Detecting Statistical Errors: Implications for Authors, Reviewers, and Editors.Journal of Korean medical science · 2025Review
- Article
- Evaluation of a method to measure the ratio of pelvic limb to thoracic limb girth in dogs.Veterinary evidenceArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe objective of this study was to assess the accuracy of Chat Generative Pre-trained Transformer 4.0 (ChatGPT-4; OpenAI, San Francisco, CA ) compared to Statistical Product and Service Solutions (SPSS; IBM SPSS Statistics for Windows, Armonk, NY) in performing statistical analyses commonly used in medical and dental research.
methodsThe datasets were analysed using SPSS (version 26) and ChatGPT-4. Statistical tests included the independent t-test, paired t-test, ANOVA, chi-square test, Wilcoxon signed-rank test, Mann-Whitney U test, Pearson and Spearman correlation, regression analysis, kappa statistic, intraclass correlation coefficient (ICC), Bland-Altman analysis, and sensitivity and specificity analysis. Descriptive statistics were used to report results, and differences between the two tools were noted.
resultsSPSS and ChatGPT-4 produced identical results for the independent sample t-test, paired t-test, and simple linear regression. In one-way ANOVA, both tools provided consistent F-values, but post-hoc analysis revealed discrepancies in mean differences and confidence intervals. Pearson chi-square and Wilcoxon signed-rank tests showed variations in p-values and Z-values. Mann-Whitney U test had differences in interquartile range (IQR), U, and Z-values. Pearson and Spearman's correlations were consistent, with IQR differences in Spearman. Sensitivity, specificity, and area under the curve (AUC) analyses were consistent, though differences in standard errors and confidence intervals were observed.
conclusionChatGPT-4 produced accurate results for several statistical tests, matching SPSS in simpler analyses. However, discrepancies in post-hoc analyses, confidence intervals, and more complex tests indicate that careful validation is required when using ChatGPT-4 for detailed statistical work. Researchers should exercise caution and cross-validate results with established tools such as SPSS.
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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.