Artificial Intelligence in Detecting Statistical Errors: Implications for Authors, Reviewers, and Editors

Choosing the right statistical tests is essential for reliable results, but errors, like picking the wrong test or misinterpreting data, can easily lead to incorrect conclusions. Research integrity implies presenting research that is honest, clear, and uses correct statistics. By identifying statistical errors, artificial intelligence (AI) systems such as Statcheck and GRIM-Test increase the reliability of research and assist reviewers. AI helps non-experts analyze data, but it can be unpredictable for experts dealing with complex data analysis. Still, its ease of use and growing abilities show promise. Recent studies show that AI is increasingly helpful in research, assisting in spotting errors in methodology, citations, and statistical analyses. Tools like LLMs, Black Spatula, YesNoError, and GRIM-Test improve accuracy, but they need good data and human checks. AI has moderate accuracy overall but performs better in controlled settings. The Statcheck and GRIM-Test are especially good at spotting statistical errors. As more studies are retracted, AI offers helpful, albeit imperfect, support. It can speed up peer review and reduce reviewer workload, but it still has limits, such as bias and a lack of expert judgment. AI also brings risks like misreading results, ethical issues, and privacy concerns, so editors must make final decisions. To use AI safely and effectively, large, well-labeled datasets, teamwork across fields, and secure systems are required. Human oversight is always necessary to review research processes and ensure their reliability; humans must make the final decision and utilize AI responsibly.