A Type II error, also known as a false negative error, is a statistical term used in hypothesis testing. It occurs when the null hypothesis is incorrectly accepted despite it being false. In other words, it is the failure to reject a null hypothesis that is actually incorrect.
When performing hypothesis tests, researchers aim to either reject or fail to reject the null hypothesis based on the evidence presented. A Type II error is a situation where there is insufficient evidence to conclude that the null hypothesis is false, leading to the erroneous acceptance of the null hypothesis.
Type II errors are characterized by a lack of statistical power, as they often occur when a sample size is too small or when there is not enough variability in the data to detect a significant difference. These errors can have significant consequences, such as withholding drug approval in clinical trials when the drug is actually effective or failing to detect a treatment's effectiveness in a scientific study.
To minimize the occurrence of Type II errors, researchers often aim to increase the sample size, use a more sensitive test, or adjust the level of significance. However, it is important to note that reducing the probability of Type II errors may increase the chances of Type I errors, where the null hypothesis is wrongly rejected.
In summary, a Type II error occurs when a null hypothesis is erroneously accepted despite it being false, primarily due to inadequate evidence or statistical power.
