Berkson's Paradox: How Sampling Bias Can Lead to False Correlations
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Berkson's Paradox demonstrates how sampling bias can create false correlations between independent variables. This occurs when the sample excludes cases where neither variable occurs.
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The Paradox shows that conditioning on the occurrence of at least one variable changes their probabilistic relationship, making independent events appear dependent.
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Examples where Berkson's Paradox could emerge include credit scoring, social media algorithms, and job screening tools. Biased data leads to skewed models.
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Machine learning experts must be aware of this Paradox and take steps to ensure representative, comprehensive training data to avoid creating unfair, inaccurate systems.
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Understanding sampling bias and false correlations is critical for building robust machine learning models that reflect the intricacies of the real world.