What is meant by the alpha level in hypothesis testing?

The alpha level in hypothesis testing is a critical concept that refers to the threshold for deciding whether to reject the null hypothesis. By convention, the alpha level is commonly set at 0.05, indicating that there is a 5% risk of rejecting the null hypothesis when it is actually true. This sets the probability of making a Type I error, which occurs when a true null hypothesis is incorrectly rejected. Thus, the alpha level serves two important purposes: it quantifies the acceptable risk of a Type I error and establishes the threshold for what is considered statistically significant.

When the p-value obtained from statistical tests is less than or equal to the alpha level, the result is deemed statistically significant, leading researchers to reject the null hypothesis in favor of the alternative hypothesis. Therefore, the alpha level signifies both the probability of making a Type I error and acts as the threshold for determining statistical significance. This dual significance leads to the conclusion that both aspects are encompassed under the definition of the alpha level.