Which action will reliably increase the power of a statistical test?

Which action will reliably increase the power of a statistical test?

• A. Increase sample size
• B. Increase alpha to 0.1
• C. Increase the effect size through manipulation
• D. Decrease population variance

Answer: (A)

Power is the probability of detecting a real effect when it exists. It rises when the signal stands out clearly against the noise in your data. Increasing the sample size reduces the standard error, which makes the sampling distribution of the test statistic under the alternative move further away from the null. With a tighter, more precise estimate, the observed effect is more likely to cross the rejection threshold, boosting power reliably. Raising the alpha level to 0.1 makes it easier to reject the null, but it sacrifices control over false positives, so it’s not a clean or universally acceptable way to increase power. Attempting to increase the effect size by manipulation isn’t a reliable or ethical route for improving power, since real-world effects aren’t something you can safely force to grow. Reducing population variance can improve power by making the data more consistent, but you generally can’t guarantee such a change in the population; tighter measurement or better design can help, but it’s not as universally dependable as simply collecting more data.

Power is the probability of detecting a real effect when it exists. It rises when the signal stands out clearly against the noise in your data. Increasing the sample size reduces the standard error, which makes the sampling distribution of the test statistic under the alternative move further away from the null. With a tighter, more precise estimate, the observed effect is more likely to cross the rejection threshold, boosting power reliably.

Raising the alpha level to 0.1 makes it easier to reject the null, but it sacrifices control over false positives, so it’s not a clean or universally acceptable way to increase power. Attempting to increase the effect size by manipulation isn’t a reliable or ethical route for improving power, since real-world effects aren’t something you can safely force to grow. Reducing population variance can improve power by making the data more consistent, but you generally can’t guarantee such a change in the population; tighter measurement or better design can help, but it’s not as universally dependable as simply collecting more data.