Free Tool
Statistical Power Analysis
Calculate statistical power or required sample size for your study.
What is Statistical Power?
Statistical power (1 - β) is the probability that a test will correctly reject the null hypothesis when it is actually false. In other words, it's the ability of your study to detect a real effect if one exists.
A power of 80% means there is an 80% chance of finding a statistically significant result if the effect truly exists. Most journals and ethics committees require a power of at least 80%.
Cohen's Effect Size Conventions
| Test | Small | Medium | Large |
|---|---|---|---|
| T-Test (Cohen's d) | 0.20 | 0.50 | 0.80 |
| ANOVA (Cohen's f) | 0.10 | 0.25 | 0.40 |
| Chi-Square (Cohen's w) | 0.10 | 0.30 | 0.50 |
| Correlation (r) | 0.10 | 0.30 | 0.50 |
Four Components of Power Analysis
📊
Effect Size
The magnitude of the difference or relationship you expect to find.
👥
Sample Size (n)
The number of observations or participants in each group.
⚡
Power (1 - β)
The probability of detecting a real effect. Usually set at 80% or higher.
🎯
Significance Level (α)
The probability of a Type I error (false positive). Usually 0.05.
Important Notes
- •Power analysis should be performed BEFORE data collection, during the study design phase.
- •This calculator uses normal approximation methods that are accurate for most practical sample sizes (n > 5).
- •If you don't know the expected effect size, consider running a pilot study or using Cohen's conventions as a starting point.
- •For complex designs (factorial ANOVA, mixed models, etc.), we recommend using our professional consulting services.