How to Calculate the Power Analysis
Power analysis is a crucial step in designing experiments and clinical trials. It helps determine the sample size required to detect an effect of a given size with a specified level of confidence. This calculator simplifies the process, allowing you to quickly and accurately perform power analysis.
- Enter the effect size you want to detect.
- Select the significance level (alpha).
- Enter the desired power (1 – beta).
- Click ‘Calculate’.
The power analysis is based on the following formula:
n = [(Z_α/2 + Z_β) / E]²
Where:
- n is the sample size
- Z_α/2 is the critical value of the normal distribution at the significance level (alpha)
- Z_β is the critical value of the normal distribution at the power (1 – beta)
- E is the effect size
| Effect Size | Significance Level (α) | Power (1 – β) | Sample Size (n) |
|---|---|---|---|
| 0.5 | 0.05 | 0.8 | 64 |
| 0.3 | 0.01 | 0.9 | 167 |
- Always use the smallest effect size you expect to detect.
- Be realistic about your power. A power of 0.8 is common, but 0.9 may be more appropriate for critical decisions.
- Consider using a software tool for complex power analysis.
What is power in statistics?
Power in statistics is the probability that a test will reject the null hypothesis when the alternative hypothesis is true.
What is effect size?
Effect size is a measure of the magnitude of a phenomenon. In power analysis, it’s the smallest effect you want to detect.
For more information, see the National Institutes of Health guide on power analysis and the University of North Carolina’s power analysis guide.