Power of a test
The power of a test is the probability it correctly rejects a false null hypothesis, equal to 1 − P(Type II error). Power increases with larger samples, bigger true effects, or a higher significance level.
FrameworkHypothesis testing
See it move
The power of a hypothesis test is 1 − β, the probability of correctly detecting an effect that genuinely exists. A test with only 30% power misses a real effect 70% of the time, so researchers typically design studies to reach at least 80% power before collecting data. Power rises with a larger sample size, a bigger true effect, or a higher significance level.
The formula
Variables
- Probability of a Type II error: failing to reject a false H₀
- Probability of correctly rejecting a false H₀
Power increases with larger sample size, a larger true effect size, or a higher significance level α. A power of 0.80 or above is the conventional minimum target.