The term 'power' of a study (1−β) refers to:

• A The probability of rejecting the null hypothesis when it is actually true (Type I error)
• B The width of the confidence interval around the effect estimate
• C The significance level set by the investigator (α)
• D The probability of detecting a true effect (correctly rejecting a false null hypothesis) ✓

Explanation

Statistical power (1−β) is the probability of correctly rejecting the null hypothesis when a true effect of a specified size exists — i.e., the probability that the study will detect a real difference if one truly exists. β is the probability of a Type II error (failing to detect a true effect). Conventionally, power is set at ≥80% (β=0.20) or ≥90% (β=0.10). Power is influenced by sample size, effect size, significance level (α), and outcome variability. Type I error (α) is the probability of a false positive, not power.

Reference: Park's Textbook of Preventive and Social Medicine, 27th ed.

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