What is the maximum probability of a Type I error set in advance called?

The maximum probability of a Type I error set in advance is known as Alpha. In statistical hypothesis testing, Alpha represents the threshold for determining whether to reject the null hypothesis. Specifically, it is the probability of incorrectly rejecting the null hypothesis when it is actually true, which leads to a Type I error.

Researchers set an Alpha level prior to conducting their tests, commonly at values such as 0.05 or 0.01, indicating that they accept a 5% or 1% risk, respectively, of making a Type I error. This concept is crucial in evaluating the significance of the results obtained from a study or experiment. By choosing an Alpha level, researchers define the likelihood of false positives in their findings, thereby guiding decisions on what constitutes sufficient evidence to reject the null hypothesis.

Other options, such as Beta, refer to the probability of a Type II error, which involves failing to reject the null hypothesis when it is false. Sample size relates to the number of observations in a study, influencing statistical power but not directly defining the Type I error probability. The null hypothesis is the statement being tested, and while it is essential in hypothesis testing, it does not define the maximum probability of a Type I error.