Alpha is defined as which of the following?

Alpha is defined as which of the following?

• A. The probability of Type II error
• B. The probability of Type I error researcher is willing to accept
• C. The probability of achieving statistical significance
• D. The observed p-value in the test

Answer: (B)

Alpha represents the level of significance in a hypothesis test. It defines the probability of making a Type I error—the chance of rejecting the true null hypothesis—that the researcher is willing to accept. This threshold is set before data collection and acts as the criterion for declaring statistical significance: if the p-value is at most alpha, we reject the null; if it’s larger, we do not. Common values are 0.05 or 0.01. This concept is different from the probability of a Type II error (beta), from the idea of “the probability of achieving statistical significance” (which depends on effect size, sample size, and variability), and from the observed p-value itself, which is the data-driven value you compute after the test.

Alpha represents the level of significance in a hypothesis test. It defines the probability of making a Type I error—the chance of rejecting the true null hypothesis—that the researcher is willing to accept. This threshold is set before data collection and acts as the criterion for declaring statistical significance: if the p-value is at most alpha, we reject the null; if it’s larger, we do not. Common values are 0.05 or 0.01. This concept is different from the probability of a Type II error (beta), from the idea of “the probability of achieving statistical significance” (which depends on effect size, sample size, and variability), and from the observed p-value itself, which is the data-driven value you compute after the test.