What is statistical power, and what factors influence it?

• A. Power is the probability of detecting a true effect; influenced by sample size, effect size, alpha level, and measurement reliability.
• B. Power is the probability that the null hypothesis is true; influenced by p-values.
• C. Power is the likelihood that a study detects any effect, regardless of size.
• D. Power is determined solely by the sample size.

Answer: (A)

Statistical power is the probability that a study will detect a real effect if one exists. In other words, it reflects how likely you are to find a true difference or relationship when there actually is one, reducing the chance of a false negative.

Power increases with several key factors. Larger sample size gives a more precise estimate and makes it easier to pick up real effects. A larger true effect size is easier to detect than a small one, so power goes up when the effect is bigger. The alpha level matters too: using a more stringent threshold (smaller alpha) makes it harder to declare significance, lowering power, whereas a less strict threshold increases power (if that change is justifiable). Measurement reliability matters because more noise in the data (lower reliability) obscures real effects, reducing power; cleaner, more reliable measurements raise power. By adjusting these factors—growing the sample, focusing on detectable effect sizes, improving measurement quality, or balancing the alpha level—you can influence the study’s power.

The other statements don’t fit because power is not the probability that the null hypothesis is true, nor is it about detecting any effect regardless of size, and it isn’t determined solely by sample size.