How do Type I and Type II errors relate to the power of a statistical test?

The power of a test is the probability that it correctly rejects a false null hypothesis (true positive). It's the complement of a Type II error. As Type I error probability increases, power also increases because we're more willing to reject the null hypothesis. However, a Type II error decreases power because it's a missed opportunity to reject a false null hypothesis.