A Type II error occurs in statistical hypothesis testing when a researcher fails to reject a null hypothesis that is actually false. This means that the test does not detect an effect or difference when one truly exists. For example, if a new medication is tested and found to be ineffective when it actually works, this is a Type II error.
The probability of making a Type II error is denoted by the symbol β (beta). Reducing the likelihood of a Type II error often involves increasing the sample size or the power of the test, which enhances the ability to detect true effects.