Calculate statistical power, effect size, and sample size for your research studies
Start Power AnalysisComprehensive statistical power calculations for all research designs
Calculate the probability of detecting true effects in your study. Ensure adequate power (≥80%) for reliable research outcomes.
Compute Cohen's d, eta squared, correlation coefficients, and other effect size measures for meaningful interpretation.
Determine optimal sample sizes to achieve desired statistical power while minimizing research costs and time.
Support for t-tests, ANOVA, regression, correlation, chi-square, and other statistical procedures.
Explore how changes in parameters affect statistical power and study design decisions.
Calculations follow established statistical standards and are suitable for academic and clinical research.
Choose the right power analysis tool for your research design
Calculate power for one-sample, two-sample, and paired t-tests. Includes Cohen's d effect size calculations.
Calculate T-Test Power →Power calculations for one-way, two-way, and repeated measures ANOVA designs with eta squared effect sizes.
Calculate ANOVA Power →Power analysis for linear and multiple regression models, including R² and partial correlation effects.
Calculate Regression Power →Determine power for correlation studies and calculate required sample sizes for detecting relationships.
Calculate Correlation Power →Power calculations for chi-square tests of independence and goodness of fit with Cramer's V effect sizes.
Calculate Chi-Square Power →Specialized power analysis for clinical trials, including survival analysis and binary endpoint studies.
Calculate Clinical Power →Statistical power is the probability that a study will detect a true effect when it exists. It's calculated as 1 - β (beta), where β is the Type II error rate. Power analysis helps researchers design studies with adequate sensitivity to detect meaningful effects.
Power analysis involves four interconnected components:
Effect sizes provide standardized measures of the magnitude of differences:
A Priori Power Analysis: Conducted before data collection to determine required sample size for desired power level.
Post Hoc Power Analysis: Conducted after data collection to determine the power of completed studies.
Sensitivity Analysis: Determines the minimum detectable effect size given fixed sample size and power.
Conventionally, statistical power of 0.80 (80%) is considered adequate, meaning there's an 80% chance of detecting a true effect if it exists. Some fields require higher power (90% or 95%) for critical studies, especially in clinical research where missing important effects could have serious consequences.
Effect size should be based on: (1) Previous research in your field, (2) Pilot study results, (3) Minimum clinically important difference, or (4) Conventional benchmarks (small, medium, large). Cohen's conventions provide starting points, but field-specific effect sizes are preferred when available.
Conduct a priori power analysis during study planning to determine required sample size. Post hoc power analysis can be useful for interpreting non-significant results, but should be interpreted cautiously. Sensitivity analysis helps understand the minimum detectable effects in your study.
Statistical power increases with larger sample sizes. The relationship is non-linear - doubling sample size doesn't double power. Very large samples can detect trivially small effects, while very small samples may miss important effects. Power analysis helps find the optimal balance.
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