Type II Errors

A Type II error occurs when a null hypothesis is accepted when it is false. This means that the test failed to reject the null hypothesis, even though it is actually false.

Type II errors are often caused by low statistical power. Statistical power is the probability of rejecting a false null hypothesis. The higher the statistical power, the lower the probability of a Type II error.

There are a number of ways to increase statistical power. One way is to increase the sample size. Another way is to use a more powerful statistical test.

It is important to note that Type II errors are not always bad. In some cases, it may be more important to avoid a Type I error (rejecting a true null hypothesis). For example, in medical testing, it is more important to avoid incorrectly diagnosing a patient with a disease than it is to incorrectly diagnose a patient as healthy.

In other cases, it may be more important to avoid a Type II error. For example, in a clinical trial, it is more important to correctly identify a new treatment that is effective than it is to avoid incorrectly identifying a new treatment that is not effective.

The decision of whether to focus on avoiding Type I or Type II errors depends on the specific situation.