Statistical Power Calculator
Calculate statistical power and required effect size for hypothesis testing
About this calculator
A Statistical Power Calculator helps researchers determine the probability of correctly detecting a true effect in their hypothesis testing. This tool calculates statistical power (the likelihood of avoiding Type II errors) and determines the minimum effect size needed for reliable results. It's essential for experimental design, sample size planning, and ensuring your study has sufficient sensitivity to detect meaningful differences when they actually exist.
How to use
Enter your significance level (alpha), sample size, and either the effect size or desired power level. Select your test type (one-tailed or two-tailed) and the calculator will compute the missing parameter. Use the results to optimize your study design before data collection begins.
Frequently asked questions
What is statistical power in hypothesis testing?
Statistical power is the probability of correctly rejecting a false null hypothesis, typically aimed to be 0.80 or higher in research studies.
What's considered an adequate effect size?
Effect sizes are context-dependent, but Cohen's conventions suggest 0.2 (small), 0.5 (medium), and 0.8 (large) for standardized effect sizes.
Why is power analysis important before collecting data?
Power analysis prevents underpowered studies that waste resources and helps determine appropriate sample sizes for detecting meaningful effects reliably.