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).
EverydayConcepts.io
Reference entry — no illustration yet
Origin
Developed by statisticians Jerzy Neyman and Egon Pearson in the early 20th century.