Final answer:
The "alpha level" refers to the level of significance in hypothesis testing, which is the preset probability of making a Type I error by rejecting the null hypothesis when it is true. It is chosen before collecting data and common levels are 0.01, 0.05, and 0.10.
Step-by-step explanation:
The term "alpha level" refers to the level of significance when testing a hypothesis. This is the probability of committing a Type I error, which occurs when the null hypothesis is incorrectly rejected. In hypothesis testing, the alpha level is denoted by the Greek letter α and it represents a threshold against which the p-value of the test statistic is compared. If the p-value is less than the alpha level, the null hypothesis is rejected.
For example, if a hypothesis test is conducted at an alpha level of 0.05, it means that there is a 5% risk of rejecting the null hypothesis when it is true. This preset alpha level is chosen by the researcher before collecting any sample data and can differ based on the required confidence in the conclusions. Common alpha levels include 0.01, 0.05, and 0.10.