Statistical power quantifies a clinical trial's ability to identify true treatment effects, serving as a critical measure of study adequacy. A trial with 80% power has an 80% probability of demonstrating statistical significance if the true treatment effect matches assumptions used in the power calculation. Conversely, such a trial has a 20% probability of failing to detect a real effect, a Type II error. Higher power reduces the risk of false-negative conclusions but requires larger sample sizes and greater resources.

Power is determined by the interplay of several factors that can be manipulated in trial design. Larger sample sizes increase power by providing more precise estimates of treatment effects. Larger true treatment effects are easier to detect, requiring less power for the same sample size. Lower variability in outcomes improves precision and increases power. More stringent significance thresholds reduce power because they require stronger evidence to declare significance. Understanding these relationships enables investigators to design trials that balance scientific requirements against practical constraints.

Adequate power is essential for ethical trial conduct because underpowered studies may fail to detect beneficial treatments, potentially delaying or preventing patient access to effective therapies. Regulatory guidance typically recommends power of at least 80% to 90% for confirmatory trials, though the appropriate power level depends on the consequences of false-negative conclusions in the specific clinical context. Post-hoc power calculations after study completion, using observed effects rather than assumed effects, are generally discouraged as they can be misleading.

Statistical power quantifies a clinical trial's ability to identify true treatment effects, serving as a critical measure of study adequacy. A trial with 80% power has an 80% probability of demonstrating statistical significance if the true treatment effect matches assumptions used in the power calculation. Conversely, such a trial has a 20% probability of failing to detect a real effect, a Type II error. Higher power reduces the risk of false-negative conclusions but requires larger sample sizes and greater resources.

Power is determined by the interplay of several factors that can be manipulated in trial design. Larger sample sizes increase power by providing more precise estimates of treatment effects. Larger true treatment effects are easier to detect, requiring less power for the same sample size. Lower variability in outcomes improves precision and increases power. More stringent significance thresholds reduce power because they require stronger evidence to declare significance. Understanding these relationships enables investigators to design trials that balance scientific requirements against practical constraints.

Adequate power is essential for ethical trial conduct because underpowered studies may fail to detect beneficial treatments, potentially delaying or preventing patient access to effective therapies. Regulatory guidance typically recommends power of at least 80% to 90% for confirmatory trials, though the appropriate power level depends on the consequences of false-negative conclusions in the specific clinical context. Post-hoc power calculations after study completion, using observed effects rather than assumed effects, are generally discouraged as they can be misleading.