False positives = 0.04 × 1,900 = <<0.04*1900=76>>76

Fri, 01 May 2026 04:56:05 +0000

Understanding False Positives in Data Analysis: Why 0.04 × 1,900 Equals 76 In data analysis, statistics play a critical role in interpreting results and making informed decisions. One common misconception involves the calculation of false positives, especially when dealing with thresholds, probabilities, or binary outcomes. A classic example is the product 0.04 × 1,900 = 76, which appears simple at first glance but can mean a lot when properly understood. ### What Are False Positives? A false positive occurs when a test incorrectly identifies a positive result when the true condition is negative. For example, in medical testing, a false positive might mean a patient tests positive for a disease despite actually being healthy. In machine learning, it refers to predicting a class incorrectly—like flagging a spam email as non-spam.