Power Analysis Calculator: Pearson Correlation
Introduction & Importance
Power analysis for Pearson correlation is a crucial step in planning your research. It helps you determine the sample size required to detect an effect of a given magnitude with a specified level of confidence.
How to Use This Calculator
- Enter the desired significance level (α), effect size (r), and sample size (n).
- Click the “Calculate” button.
- View the results and power chart below.
Formula & Methodology
The power calculation is based on the following formula:
Power = 1 – β = Z_α/2 + Z_β * √[(1 + (n – 3) * r^2) / (n * (1 – r^2))]
Real-World Examples
Data & Statistics
| Effect Size (r) | Power (80% confidence, n=50) |
|---|---|
| 0.2 | 0.18 |
| 0.3 | 0.46 |
| 0.4 | 0.73 |
Expert Tips
- Start with a reasonable effect size based on previous research or theory.
- Consider the trade-off between power and sample size.
- Be mindful of the assumptions underlying the power calculation.
Interactive FAQ
What is the default significance level?
The default significance level is 0.05.
What does the effect size (r) represent?
The effect size (r) represents the strength and direction of the linear relationship between two variables.
For more information, see the Pearson correlation coefficient guide from Statistics How To.
Learn more about power analysis in this paper from the U.S. National Library of Medicine.