An increase in the risk of type I error also increases the risk of a type II error.
true or false
False.
An increase in the risk of a type I error (also known as a false positive) does not increase the risk of a type II error (also known as a false negative); in fact, the two types of errors are inversely related.
Type I error occurs when the null hypothesis is true but is incorrectly rejected. The risk of committing a type I error is denoted by the alpha level (α), which is usually set at 0.05 or 5%, meaning there is a 5% chance of rejecting a true null hypothesis.
Type II error occurs when the null hypothesis is false but is incorrectly failed to be rejected. The risk of committing a type II error is denoted by the beta level (β).
When you increase the alpha level, making it easier to reject the null hypothesis, you reduce the possibility of missing a true effect (thus decreasing the risk of a type II error). Conversely, if you decrease the alpha level to be very conservative against false positives, you increase the chances of a type II error by making it harder to detect a true effect when it exists.
Thus, a change in the criterion for rejecting the null hypothesis (alpha level) has an inverse effect on the risks of the two types of errors.