Final answer:
The probability of a Type I error in hypothesis testing is denoted by alpha (α), and it represents the risk of falsely rejecting a true null hypothesis. The probability of a Type II error is denoted by beta (β), corresponding to the risk of failing to reject a false null hypothesis. The power of the test, 1 – β, is the likelihood of correctly rejecting a false null hypothesis.
Step-by-step explanation:
The probability of a Type I error is denoted as alpha (α). A Type I error occurs when the null hypothesis is incorrectly rejected when it is actually true. In hypothesis testing, it's expressed as P(Type I error), which is the probability of rejecting the null hypothesis when the null hypothesis is true. On the other hand, the probability of a Type II error is denoted by beta (β), which is the error made when the null hypothesis is not rejected, but it is actually false. The power of the test, which is 1 – β, measures the test's ability to correctly reject a false null hypothesis.Both alpha and beta represent probabilities of committing respective errors in hypothesis testing, with alpha representing the level of significance of the test. Ideally, researchers aim to minimize these errors to ensure the reliability of the test's outcomes.