Statistical Power Analysis Calculator
Determine the sample size needed to detect an effect of a given size with a specified level of confidence. Power analysis is essential for planning experiments and clinical trials.
Statistical power is the probability that a test correctly rejects a false null hypothesis (i.e., detects a real effect). Power analysis helps you determine how many participants you need before running your study.
Key parameters: Effect size (Cohen's d) measures the magnitude of the difference in standard deviation units. The significance level (alpha) is the acceptable false positive rate. Power (1 - beta) is the desired probability of detecting the effect. A standard setup is d = 0.5, alpha = 0.05, power = 0.80.
Cohen's d guidelines: Small = 0.2 (subtle effect, large sample needed), Medium = 0.5 (moderate effect, practical significance), Large = 0.8 (obvious effect, smaller sample sufficient). These are conventions; the appropriate effect size depends on your field and research question. An underpowered study wastes resources and may miss real effects, while an overpowered study may detect trivially small effects.