Power Calculator
Plan sample sizes, minimum detectable effects and power for sixteen experimental and quasi-experimental designs. Every number comes from published formulas; an AI assistant helps you choose assumptions and explains the results.
Solve for
Outcome type
Effect-size converter
Enter values to convert.
Power curve
Sensitivity table
Take your design with you
Copy text for a pre-analysis plan, code to reproduce the calculation, or a link that reopens these exact settings.
Methods
How each design is calculated
References
- Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies. Journal of the American Statistical Association.
- Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association.
- Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research.
- Bloom, H. S. (2006). The core analytics of randomized experiments for social research. MDRC.
- Bloom, H. S., Richburg-Hayes, L., & Black, A. R. (2007). Using covariates to improve precision for studies that randomize schools to evaluate educational interventions. Educational Evaluation and Policy Analysis.
- Burlig, F., Preonas, L., & Woerman, M. (2020). Panel data and experimental design. Journal of Development Economics.
- Chinn, S. (2000). A simple method for converting an odds ratio to effect size for use in meta-analysis. Statistics in Medicine.
- Chow, S.-C., Shao, J., & Wang, H. (2008). Sample Size Calculations in Clinical Research. Chapman & Hall/CRC.
- Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum.
- Dong, N., & Maynard, R. (2013). PowerUp!: A tool for calculating minimum detectable effect sizes and minimum required sample sizes. Journal of Research on Educational Effectiveness.
- Duflo, E., Glennerster, R., & Kremer, M. (2007). Using randomization in development economics research: A toolkit. Handbook of Development Economics.
- Eldridge, S. M., Ashby, D., & Kerry, S. (2006). Sample size for cluster randomized trials: Effect of coefficient of variation of cluster size and analysis method. International Journal of Epidemiology.
- Hedges, L. V., & Hedberg, E. C. (2013). Intraclass correlations and covariate outcome correlations for planning two- and three-level cluster-randomized experiments in education. Evaluation Review.
- Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics.
- Hussey, M. A., & Hughes, J. P. (2007). Design and analysis of stepped wedge cluster randomized trials. Contemporary Clinical Trials.
- Lee, D. S., McCrary, J., Moreira, M. J., & Porter, J. (2022). Valid t-ratio inference for IV. American Economic Review.
- McKenzie, D. (2012). Beyond baseline and follow-up: The case for more T in experiments. Journal of Development Economics.
- Muralidharan, K., Romero, M., & Wüthrich, K. (2023). Factorial designs, model selection, and (incorrect) inference in randomized experiments. Review of Economics and Statistics.
- Schochet, P. Z. (2008). Statistical Power for Regression Discontinuity Designs in Education Evaluations. Institute of Education Sciences.
- Schuirmann, D. J. (1987). A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. Journal of Pharmacokinetics and Biopharmaceutics.
- Senn, S. (2002). Cross-over Trials in Clinical Research. Wiley.
- Staiger, D., & Stock, J. H. (1997). Instrumental variables regression with weak instruments. Econometrica.
- Stock, J. H., & Yogo, M. (2005). Testing for weak instruments in linear IV regression. In Identification and Inference for Econometric Models. Cambridge University Press.
- Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science.
- Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics.
- Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data. MIT Press.
FAQ
Common questions
How accurate are the numbers?
They use the published formulas listed under Methods, with t-distribution critical values for small samples, and were checked against G*Power, Stata's power commands, independent recalculations and simulations. Designs marked "Approximation" rely on simplifying assumptions that are explained on the page; treat them as planning guides.
Does the AI calculate my sample size?
No. The assistant explains concepts, helps you choose assumptions and suggests calculator settings. Every number comes from the calculator, and settings only change when you press "Apply to calculator".
What happens to my data?
Calculations run in your browser. Your conversation and settings are saved only in your own browser so you can return to them. Chat messages are sent to the AI provider to generate replies and are not stored on this site's server. Avoid sharing confidential information in the chat.
How do I cite this tool?
Cite the methodological source shown for your design (it is included in the pre-registration paragraph), and optionally: Us-Salam, D. (2026). Power Calculator. https://danishussalam.github.io/power-calculator.html
What are the main limitations?
Power depends on assumptions you supply, especially the effect size, intra-cluster correlation and autocorrelation, so use pilot or baseline data where possible and check the sensitivity table. Complex designs (for example staggered adoption with heterogeneous effects) may need simulation-based power analysis.