Failing to reject a false null hypothesis — a missed effect.A Type II error, also known as a false negative, occurs when a statistical test fails to reject a false null hypothesis. In other words, you miss a real effect or difference that actually exists. While Type I errors are about false discoveries, Type II errors are about missed opportunities—failing to detect something that is truly there.
The distinction between Type I and Type II errors was formalized by Jerzy Neyman and Egon Pearson in their 1928–1933 papers on hypothesis testing, building on earlier work by Ronald Fisher.
The probability of making a Type II error is denoted by the Greek letter beta (β). It depends on several factors: the true size of the effect, the sample size, the variability in the data, and the chosen significance level. The power of a test, defined as 1 − β, is the probability of correctly rejecting a false null hypothesis. To reduce Type II errors, researchers increase sample sizes, reduce measurement noise, or use more sensitive statistical methods.
Critical in clinical trials (avoiding approval of ineffective drugs is Type I, but missing a beneficial drug is Type II), in screening tests (e.g., cancer screening), and in any research where failing to detect a real effect has consequences.
In medical screening, a Type II error is what happens when a mammogram misses a tumor that is actually present—a false negative that can delay treatment.