Researchers investigated the sourcing of numerical facts provided by leading AI assistants, including ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. They posed 200 questions requiring statistical answers and observed that AI models often provided identical figures, even when the underlying research was absent or untraceable. These unverified numbers are termed 'zombie stats' because they are repeatedly cited without a foundational study.
The study involved tracing 1,285 statistics cited by these AI assistants. For each statistic, researchers followed the provided citations up to five pages deep. An example given was the statistic '44% of salespeople give up after one follow-up,' which all five assistants provided but ultimately led to no original research, instead pointing to blog posts and writing samples.
Out of the 1,285 statistics examined, only 40% could be traced back to a study that supported the stated figure. Another 8% reached a study that contradicted or did not fully support the statistic. The remaining statistics either led to dead ends or sources that themselves lacked original research. This indicates a significant portion of AI-generated statistics lack robust evidentiary backing.
The issue is compounded by the confident tone with which AI assistants present these statistics, making it difficult for users to discern between well-researched data and 'zombie stats.' All five assistants presented unverified numbers with the same confidence and citation style as verifiable ones. This highlights a challenge in relying on AI for factual numerical information without independent verification.
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A study found that AI assistants like ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews frequently cite statistics that lack original research, dubbed 'zombie stats'. Researchers traced 1,285 statistics and found that 40% led to a study supporting the claim, while others ended without verifiable research. This matters because AI models present these unverified numbers with confidence, making it difficult for users to distinguish them from well-sourced data.