What does the 'alpha level' indicate in hypothesis testing?

The alpha level, often denoted as α, represents the predetermined threshold for statistical significance in hypothesis testing. It is a critical value that establishes the criterion for deciding whether to reject the null hypothesis. Commonly set at values like 0.05 or 0.01, the alpha level signifies the probability of committing a Type I error, which occurs when the null hypothesis is rejected even though it is true. By setting this threshold before analyzing the data, researchers ensure that the likelihood of finding results due to random chance is kept at a specific level, thus allowing for a rigorous evaluation of the data's significance.

This understanding of the alpha level is crucial in interpreting the results of statistical tests since it provides a benchmark against which the p-value (the probability of obtaining the observed results if the null hypothesis is true) is compared. If the p-value is less than the alpha level, it suggests that the observed data are statistically significant and leads to the rejection of the null hypothesis.