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A memorable illustration of statistical errors involves medical diagnoses. A Type I error (a false positive) is akin to a doctor examining a male patient and incorrectly declaring, 'You are pregnant.' The doctor has claimed to detect a condition that does not actually exist. Conversely, a Type II error (a false negative) is akin to a doctor examining a visibly pregnant female patient and incorrectly concluding, 'You are not pregnant.' In this case, the doctor has failed to detect a condition that is genuinely present.

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