In statistical hypothesis testing, choosing the significance level (often denoted as alpha or ) is a crucial step that helps determine the threshold for rejecting the null hypothesis. It represents the maximum probability of rejecting the null hypothesis when it is actually true, also known as a Type I error.
The choice of significance level involves balancing the risk of false positives (rejecting the null hypothesis when it is true) and false negatives (failing to reject the null hypothesis when it is false). Common significance levels include 0.05, 0.01, and 0.001, with lower values indicating a stricter threshold for rejecting the null hypothesis.