Type I and Type II Errors
False Positives Vs. False Negatives
In statistical hypothesis testing, a Type I Error is the rejection of a true null hypothesis (also known as a "false positive" finding), while a Type II Error is failing to reject a false null hypothesis (also known as a "false negative" finding).
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Origin
Jerzy Neyman and Egon Pearson introduced the distinction in their 1933 paper "On the Problem of the Most Efficient Tests of Statistical Hypotheses," building on their earlier 1928 collaboration. The framework became foundational to 20th-century frequentist statistics.
Updated February 22, 2026