Which statement describes statistical power and its importance in study design?

• A. The probability of detecting a true effect.
• B. The probability of failing to detect a true effect.
• C. The probability of making a Type II error.
• D. The probability of obtaining a significant result by chance.

Answer: (A)

The main idea being tested is what statistical power means and why it matters for planning a study. Power is the probability of detecting a true effect if one actually exists. This matters because it guides how large a sample you need and how small an effect you can reliably detect, helping to ensure the study doesn’t miss real differences (reducing the risk of a Type II error). Power is equal to 1 minus the probability of a Type II error, and in design you typically aim for a power like 0.8 or higher to have a good chance of finding meaningful effects. The other statements describe related but different concepts: failing to detect a true effect is the Type II error (beta), and the chance of obtaining a significant result by chance relates to the Type I error rate (alpha).