What is a Type II error?

A Type II error occurs when a statistical test fails to reject the null hypothesis despite the fact that the alternative hypothesis is true. This means that the test indicates there is not enough evidence to support the alternative hypothesis, even though in reality it would be valid.

In more practical terms, this can happen in situations where there is a true effect or difference present in the data, but due to factors like sample size, variability, or the power of the test, the analysis does not detect this effect. Being able to identify a Type II error is crucial in research, as it implies that potentially important findings or effects might be overlooked, leading to the conclusion that a hypothethical condition, relationship, or effect does not exist when it actually does.

The other options describe different concepts: rejecting a true null hypothesis relates to a Type I error, while accepting incorrect hypotheses doesn't accurately capture the focus of Type II errors in hypothesis testing.