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Evaluating large language models for selection of statistical test for research: A pilot study.

Himel Mondal1, Shaikat Mondal2, Prabhat Mittal3

  • 1Department of Physiology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India.

Perspectives in Clinical Research
|November 25, 2024
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Summary

Large language models (LLMs) demonstrate high accuracy in recommending statistical tests for research, achieving over 95% acceptance rates. These AI tools can effectively support researchers in selecting appropriate statistical methods.

Keywords:
Accuracyartificial intelligencedata analysislarge language modelsresearch methodologystatistical test selection

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Area of Science:

  • Artificial Intelligence in Scientific Research
  • Statistical Test Selection
  • Natural Language Processing Applications

Background:

  • Selecting appropriate statistical tests is crucial yet challenging in contemporary research.
  • Large language models (LLMs) offer potential for automating statistical test selection, improving efficiency and accuracy.

Purpose of the Study:

  • To evaluate the capability of freely available LLMs (ChatGPT3.5, Bard, Bing Chat, Perplexity) in recommending statistical tests.
  • To compare LLM statistical test recommendations against those provided by human experts.

Main Methods:

  • Developed 27 case vignettes from published literature for common research models.
  • LLMs were presented with case vignettes and asked to recommend suitable statistical tests.
  • LLM recommendations were evaluated for concordance and acceptance against expert-defined answer keys.

Main Results:

  • All evaluated LLMs demonstrated high acceptance rates (>95%) for statistical test recommendations.
  • Concordance rates varied, with Microsoft Bing Chat showing 96.3% and others above 77.78%.
  • LLMs exhibited moderate agreement among themselves, with an intra-class correlation coefficient of 0.728.

Conclusions:

  • LLMs show significant potential as decision support systems for statistical test selection in research.
  • While not replacing human expertise, LLMs can enhance the speed and accuracy of choosing statistical methods.
  • Further research may explore refining LLM capabilities for more nuanced statistical guidance.