The alpha level represents

The alpha level represents

• A. The probability of a Type II error.
• B. The probability of a Type I error.
• C. The power of the test.
• D. The effect size.

Answer: (B)

Alpha level is the threshold used to decide whether a result is statistically significant. It represents the probability of making a Type I error—rejecting the null hypothesis when it is actually true. For example, setting alpha at 0.05 means that, if there is no real effect, about 5% of studies would falsely conclude significance by chance. This is different from the probability of a Type II error (failing to detect a real effect), which is beta. The test’s power, or the probability of finding a true effect, is 1 minus beta. The magnitude of the observed difference, or effect size, is a measure of how large the effect is, not a probability. Alpha is chosen before data collection to control the rate of false positives and to balance that risk with the study’s ability to detect real effects.

Alpha level is the threshold used to decide whether a result is statistically significant. It represents the probability of making a Type I error—rejecting the null hypothesis when it is actually true. For example, setting alpha at 0.05 means that, if there is no real effect, about 5% of studies would falsely conclude significance by chance. This is different from the probability of a Type II error (failing to detect a real effect), which is beta. The test’s power, or the probability of finding a true effect, is 1 minus beta. The magnitude of the observed difference, or effect size, is a measure of how large the effect is, not a probability. Alpha is chosen before data collection to control the rate of false positives and to balance that risk with the study’s ability to detect real effects.