Which of the following is NOT a factor that typically increases statistical power?

The correct answer is that decreasing the alpha level is not a factor that typically increases statistical power. To understand this, it is essential to define what statistical power is: it is the probability of correctly rejecting the null hypothesis when it is false, meaning that a study has a high likelihood of detecting an effect if there truly is one.

Increasing the alpha level, on the other hand, means raising the threshold for significance (for example, from 0.05 to 0.10). This makes it easier to identify an effect as significant, thereby increasing the power. Conversely, decreasing the alpha level tightens the criteria for significance, which can result in a failure to reject the null hypothesis even when there is an actual effect present, thus reducing statistical power.

The other factors listed—such as increasing sample size, increasing effect size, and decreasing variance—are all well-established methods for increasing statistical power. Larger sample sizes help to improve the estimation of the population parameters and reduce variability. A larger effect size means that the true difference or relationship is more pronounced, making it easier to detect. Reducing variance leads to more precise estimates and less overlap between groups, again making it easier to find significant results.