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Data Bias Check
Check an analysis for the biases that lead to wrong conclusions.
Updated June 2026
The prompt
Review this analysis and conclusion for bias: {{analysis}}. Check for common biases that mislead: survivorship bias, selection and sampling bias, confirmation bias in interpretation, Simpson paradox in aggregated data, missing-data bias, and using a metric that does not measure what is claimed. For each potential issue explain how it could distort the conclusion and how to check or correct for it, and give a more careful read of what the data actually supports. Be the skeptical second pair of eyes that catches the wrong conclusion.
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AI Khazna
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