What is the Significance Level (alpha) in hypothesis testing?

In hypothesis testing, the Significance Level, commonly denoted as alpha (α), represents the threshold for determining whether to reject the Null Hypothesis. It is specifically defined as the probability of making a Type I error, which occurs when the Null Hypothesis is incorrectly rejected when it is actually true. Thus, a significance level of 0.05, for instance, indicates a 5% risk of concluding that there is a significant effect when, in reality, no such effect exists.

Understanding this concept is crucial for interpreting statistical results and ensuring valid conclusions. If the significance level is set too high, there is a greater chance of rejecting the Null Hypothesis unnecessarily, leading to potential misconceptions about the data. This reinforces the importance of properly setting and interpreting alpha in the context of any hypothesis testing framework.