What does the significance level (alpha) in hypothesis testing signify?

The significance level, commonly denoted as alpha, plays a crucial role in hypothesis testing. It represents the probability of making a Type I error, which occurs when the null hypothesis is rejected even though it is true. This means that when you set a significance level (for example, alpha = 0.05), you are allowing for a 5% chance that you will incorrectly conclude there is an effect or difference when there actually isn't one.

Additionally, the significance level serves as a threshold for determining statistical significance. When analyzing data, if the p-value (the probability of observing your data, or something more extreme, given that the null hypothesis is true) is less than alpha, you reject the null hypothesis. This threshold helps researchers decide whether their findings are statistically significant, so they can take appropriate actions based on the results.

Thus, both the probability of making a Type I error and the threshold for determining statistical significance are integral aspects of the significance level. This is why the correct answer encompasses both definitions.