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What are hallucinations in AI and how do they affect data analysis?
Hallucinations are confident but false statements generated by a language model, such as invented figures, nonexistent sources or wrong explanations. In data analysis they can appear as fabricated numbers, misdescribed methods or code that runs but is wrong. Always verify against source data and run the code yourself.
Models generate plausible text, not guaranteed truth. They can be especially unreliable with exact arithmetic, obscure statistics and recent events.
Reduce risk by:
- Asking for formulas or code, then checking them.
- Reconciling outputs with known totals.
- Requesting reasoning step by step and reviewing it.
- Never citing statistics from a chatbot without finding the original source.
Treat AI as a fast but fallible junior assistant, with a human accountable for the final analysis.
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