Final answer:
The alpha level, or level of significance, in hypothesis testing is the probability of making a Type I error. It is set before analyzing the data and is typically chosen to be either 0.05 or 0.01. The choice of alpha level considers the consequences of making Type I and Type II errors.
Step-by-step explanation:
The alpha level, also known as the level of significance, is the probability of a Type I error, which is the probability of rejecting the null hypothesis when it is actually true. It is denoted by the symbol a. The alpha level is set before analyzing the data and is usually chosen to be either 0.05 or 0.01, although it can vary depending on the context.
The alpha level is determined by the consequences of making Type I and Type II errors. Type I error refers to rejecting the null hypothesis when it is true, while Type II error refers to failing to reject the null hypothesis when it is false. The choice of alpha level involves balancing the risks of these two types of errors.