Type II errors (also known as False Negatives) occur when a statistical hypothesis test incorrectly fails to reject the erroneous null hypothesis. That is to say, it leads the user to make an incorrect decision to not dismiss the false null hypothesis since the test is not powerful enough to discover inconclusive information for the alternative hypothesis.
Similar to type I error, it is impossible to completely remove the error from a hypothesis. The only strategy available is to reduce the likelihood of incurring this sort of statistical mistake. Due to the strong relationship between the probability of type II error and the power of a statistical test, the chances of this error occurring may be reduced by raising the test’s power.
How to Reduce Type 2 Errors
- Increase the size of the sample. This is one of the easiest ways to improve the reliability of a test. The power of a hypothesis test to detect differences is mostly determined by the size of the sample used to test it. A larger sample size increases the chance of finding statistical differences and the test’s reliability.
- Increase the degree of significance. Another option is to focus on a smaller number. For instance, a scientist may choose a level of significance of 0.10 rather than the widely accepted criterion of 0.05. More significant evidence suggests that the null hypothesis may be rejected even when it is correct.
An increased likelihood of rejecting the null hypothesis reduces the odds of making a type II error while upping the chances of making a type I. Consequently, it is the user’s responsibility to evaluate the likelihood of making type I and type II errors and to choose an acceptable statistical significance level.