A Type II error in statistics, also known as a false negative, occurs when a hypothesis test fails to reject a null hypothesis that is actually false. In other words, it is the error of incorrectly concluding that there is no effect or no difference when, in reality, one exists
Key points about Type II error:
- It means not rejecting the null hypothesis even though it is false.
- It is called a false negative because the test misses a real effect or relationship.
- The probability of making a Type II error is denoted by beta (β).
- The statistical power of a test (1 - β) represents the probability of correctly rejecting a false null hypothesis; higher power means lower Type II error risk.
- Factors influencing Type II error include sample size, effect size, significance level, and measurement error.
- Reducing Type II errors (e.g., by increasing sample size or relaxing significance criteria) often increases the risk of Type I errors (false positives)
Example:
If a medical test fails to detect a disease in a patient who actually has it, this is a Type II error-a false negative result
. Similarly, in an A/B test for a website, concluding that a change has no impact when it actually does is a Type II error
. In summary, a Type II error is the failure to detect a true effect, leading to the mistaken acceptance of the null hypothesis when it should be rejected.
