A type 2 error is a statistical term used within the context of hypothesis testing that describes the error that occurs when one fails to reject a null hypothesis that is actually false. In other words, a type 2 error is the failure to detect a difference or effect that actually exists. It is also known as a "false negative" finding or conclusion.

For example, in a clinical trial, a type 2 error would occur if a new treatment is not found to be effective when it actually is. The probability of making a type 2 error is denoted as β, and it is related to the power of a statistical test, which is the probability of correctly rejecting a false null hypothesis.

Type 2 errors can occur if there is not enough power in statistical tests, often resulting from sample sizes that are too small. Increasing the sample size can help reduce the chances of committing a type 2 error.