Exploring ChatGPT’s Proficiency in Nonparametric Statistics: An Initial Review and Benchmark Assessment

2025-04-19T17:50:46+07:00

Artificial Intelligence (AI) is transforming education, particularly in teaching statistics, by enhancing personalized learning and feedback through tools like ChatGPT (Tulsiani, 2024). ChatGPT is an advanced artificial intelligence chatbot developed by OpenAI that uses deep learning to understand and generate human-like text. It is based on the GPT (Generative Pre-trained Transformer) model, trained on vast amounts of text data to assist with answering questions, generating content, and engaging in natural conversations. This study evaluates ChatGPT version 3.5 performance in nonparametric statistical analysis by assessing its ability to generate solutions for seven tests, including the Test of Randomness, ANOVA, Chi-Square Goodness-of-Fit Test, Median Test, Cochran’s Q Test, Wilcoxon-Mann-Whitney Test, and Binomial Probability Test. Using three prompt engineering strategies—Basic Prompt (BP), Structured Prompt (SP), and Error-Awareness Prompt (EAP)—ChatGPT's outputs are compared against manual calculations and statistical software (Jeffreys’s Amazing Statistics Program(JASP) and Excel) for accuracy, consistency, and clarity. Results show significant discrepancies in Basic Prompt outputs between November 2023 and 2024, with sum of squares values of 6421.82 and 6928.00, and an F-value of 0.93 (p = 0.53), indicating no significant difference. Similarly, the effect of prompt type is statistically insignificant (F = 1.43, p = 0.26), as is the absolute error analysis (F = 0.59, p = 0.57). However, differences in statistical test approaches are significant (F = 3.10, p = 0.04), suggesting that method selection impacts accuracy. Findings emphasize the role of structured and error-aware prompts in improving ChatGPT’s performance, highlighting the importance of effective prompt engineering in nonparametric statistics. These insights contribute to improving AI-assisted learning in statistical education and research, ensuring more reliable computational outputs. Lastly, guidelines for effective prompt engineering in Nonparametric Statistics were formulated.

Received Date: February 2, 22025
Revised Date: March 18, 2025
Accepted Date: March 30, 2025

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Morphological Evolution of the Krasnodar Reservoir Bed (2006-2021): Insights from Geomorphometric Analysis and Benthic Form Transformations

2025-04-20T11:26:02+07:00

In the Krasnodar region of the Russian Federation, a water reservoir was established along the Kuban River in 1973 and has undergone gradual siltation and significant morphological changes over the years. This study employs geomorphometry to examine the reservoir’s bathymetry and categorize its mesoscale landforms, drawing on multiple bathymetric surveys. By utilizing Digital Benthic Models (DBM) and geospatial analysis, we examine the morphological evolution from 2016 to 2021. The results reveal notable transformations in benthic forms, including the disappearance of U-shaped valleys and their transition into canyons and plains. Spatial correspondence analysis and quantitative assessments offer insights into the consistency and changes within the reservoir’s landscape. These findings not only contribute to a deeper understanding of sedimentation processes and reservoir morphometry but also have practical implications for reservoir management and environmental conservation.

Received Date: December 4, 2024
Revised Date: March 17, 2025
Accepted Date: March 27, 2025

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International Journal in Information Technology in Governance, Education and Business

Exploring ChatGPT’s Proficiency in Nonparametric Statistics: An Initial Review and Benchmark Assessment

Morphological Evolution of the Krasnodar Reservoir Bed (2006-2021): Insights from Geomorphometric Analysis and Benthic Form Transformations