type I error
statistics
Quick Summary
In statistical testing, a Type I error occurs when the null hypothesis is rejected despite being true. This is also known as a false positive. The null hypothesis is a statement that there is no significant difference between two groups or variables. When a Type I error is made, it means that a researcher concludes there is an effect or difference when, in reality, there is none.1
A Type II error, in contrast, is when the null hypothesis is accepted even though it is false. This is referred to as a false negative.2
Summary created from Britannica articles
using AI.
AI can make mistakes, so verify using Britannica articles.
2 Britannica Sources
Ask Britannica AI about this topic
Britannica doesn't have a full article on this topic, so we've curated a list of our relevant coverage.
