Which of the following statements about alpha level is true?

Which of the following statements about alpha level is true?

• A. A higher alpha reduces the risk of Type I error.
• B. Alpha level determines the likelihood of Type II error only.
• C. Alpha level has no impact on error rates.
• D. A higher alpha increases the probability of a Type I error.

Answer: (D)

Alpha level is the threshold used to decide whether an observed effect is statistically significant; it represents the probability of rejecting the null hypothesis when it is actually true—a Type I error. If you raise the alpha level, you are more willing to call results significant, which increases the chance of a false positive. In other words, a higher alpha makes Type I errors more likely. It also affects power: increasing alpha tends to increase power (and thus decrease Type II error), but the key point here is that changing alpha changes the rate of Type I errors. The other statements misstate this relationship: alpha does not reduce Type I error when it’s higher, it raises it; it does more than just influence Type II error, and it does indeed affect error rates.

Alpha level is the threshold used to decide whether an observed effect is statistically significant; it represents the probability of rejecting the null hypothesis when it is actually true—a Type I error. If you raise the alpha level, you are more willing to call results significant, which increases the chance of a false positive. In other words, a higher alpha makes Type I errors more likely. It also affects power: increasing alpha tends to increase power (and thus decrease Type II error), but the key point here is that changing alpha changes the rate of Type I errors. The other statements misstate this relationship: alpha does not reduce Type I error when it’s higher, it raises it; it does more than just influence Type II error, and it does indeed affect error rates.