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.
An increase in the risk of type I error also increases the risk of a type II error.
true or false
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