Why LLM-Driven Data Analysis Can Quietly Produce Wrong Results

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Large language models can write and execute data analysis code, but errors in the code, statistical methods, or interpretation can each produce misleading results without triggering any visible warning. Silent data loss from dropped missing values, mishandled joins, or type coercion errors can alter an analysis entirely while the code continues to run cleanly. Statistical pitfalls such as violated independence assumptions, multiple comparisons without correction, and bad covariate choices can yield false or distorted findings that the model presents with apparent confidence. Unlike a human analyst, an LLM typically lacks familiarity with the specific dataset, making it less likely to catch context-dependent anomalies. Experts suggest that routinely printing row counts at each transformation step is one of the most effective ways to surface many of these hidden data integrity issues.

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ProgrammingDEV Community ·

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