What do increased alpha levels do to statistical power?

Increasing alpha levels enhances statistical power because it raises the threshold for rejecting the null hypothesis, which allows for a greater likelihood of detecting a true effect when it exists. In hypothesis testing, a higher alpha level means that the criterion for statistical significance is more lenient. For example, increasing the alpha level from 0.05 to 0.10 means that there is a greater chance of finding a significant result, assuming that there is indeed an effect present.

Statistical power is defined as the probability of correctly rejecting the null hypothesis when it is false. By increasing the alpha level, researchers can reduce the risk of committing a Type II error (failing to reject a false null hypothesis), thereby increasing the overall power of the test. This relationship emphasizes the balance researchers must maintain between the risk of falsely identifying a significant effect (Type I error) and the ability to detect real effects (power).

Thus, increasing alpha levels directly leads to increased statistical power, making the correct answer the choice that indicates an increase.