Sample Size Calculator
Overview
The Sample Size Calculator determines the required sample size for achieving desired statistical power in your study. Choose from different test types (t-test, proportion test, correlation, ANOVA), specify your expected effect size and significance level, and instantly see how many participants you need. The interactive visualizations show the relationships between sample size, power, and effect size to help you make informed decisions about study design.
Tips
- A power of 0.80 (80%) is the conventional minimum for most research studies, meaning an 80% chance of detecting an effect if it exists
- Use Cohen’s guidelines for effect sizes: small (d=0.2), medium (d=0.5), large (d=0.8) for t-tests; adjust accordingly for other test types
- Remember that doubling your power requires roughly quadrupling your sample size due to the non-linear relationship
- For pilot studies or preliminary research, you can accept lower power (0.70) to reduce costs, but publish findings with appropriate caveats
- Always round up to the nearest whole number when calculating required sample size to ensure adequate power
- Consider practical constraints like budget and recruitment feasibility when choosing effect sizes to detect
- Smaller effect sizes require dramatically larger samples - detecting d=0.2 needs about 16 times more participants than detecting d=0.8