Which statement about the significance threshold is supported by the material?

• A. Alpha is the threshold for declaring significance.
• B. Alpha is the same as p-value.
• C. Alpha is a measure of effect size.
• D. Alpha is irrelevant to hypothesis testing.

Answer: (A)

The key idea is that alpha is the cutoff you set to decide whether a result is considered statistically significant. In hypothesis testing, you choose an alpha level (often 0.05) before collecting data. After you run the test, you obtain a p-value, which tells you the probability of seeing your data (or more extreme) if the null hypothesis is true. If the p-value is less than or equal to alpha, you reject the null and call the result significant. If it’s larger, you do not reject the null.

Alpha isn’t the same as the p-value; the p-value is computed from the data, while alpha is a preset threshold that controls the risk of a Type I error (finding a false positive). Alpha is also not a measure of effect size; it doesn’t tell you how big the observed effect is, only how willing you are to declare significance given the data. And it isn’t irrelevant—it directly governs the decision rule in hypothesis testing.