Which term describes the likelihood of a type I error in hypothesis testing?

The term that describes the likelihood of a type I error in hypothesis testing is known as the alpha level. In statistical hypothesis testing, a type I error occurs when the null hypothesis is incorrectly rejected when it is actually true. The alpha level quantifies the probability of making this error, usually set at a threshold such as 0.05 or 0.01, which indicates a 5% or 1% risk of rejecting the null hypothesis erroneously.

Understanding the alpha level is crucial because it defines the criteria for making decisions based on statistical evidence. It helps researchers determine how much risk they are willing to accept of making a type I error when they conclude that there is a statistically significant effect or difference when there isn't one.

Other terms such as the power of the test relate to the likelihood of correctly rejecting a false null hypothesis and are not directly associated with type I errors. The beta level refers to the probability of making a type II error, which involves failing to reject a false null hypothesis, while the confidence level pertains to the range within which the true parameter is expected to lie, complementing the alpha level by establishing the degree of certainty in the test results.