Ensure your research has adequate statistical power to detect meaningful effects. Our experts provide comprehensive guidance on sample size determination, power analysis, and statistical precision for rigorous, reproducible research outcomes.
Comprehensive support across all major study designs and analysis methods
T-tests, ANOVA, and MANOVA sample size determination with effect size estimation and power calculations for group mean differences.
Sample size for proportions, prevalence surveys, and binomial outcomes with precision-based and hypothesis-driven approaches.
Sample size for Pearson/Spearman correlations, linear regression, and multiple predictors with R-squared calculations.
Factorial designs, repeated measures, mixed models, and multivariate analysis of variance sample size planning.
End-to-end support for your sample size calculation needs
Define objectives & effect size
Select appropriate test & alpha level
Determine required sample size
Assess robustness & assumptions
Report methodology & justification
Provide reproducible code & logs
Tailored support for every stage of your sample size planning
Prospective sample size calculation based on desired power, effect size estimate, alpha level, and study design parameters.
Determine minimum detectable effect size for your fixed sample size, accounting for design constraints and resource limitations.
Sample size for cluster randomization, hierarchical models, repeated measures, and multi-level study designs.
Literature-based effect size extraction, meta-analytic pooling, and Cohen's guidelines for your research domain.
Retrospective power analysis for completed studies, interpreting non-significant findings, and manuscript justification.
G*Power, R (pwr package), PASS, nQuery, SAS Power, SPSS SamplePower, Stata, and custom Python calculations.
Our team of PhD-level statisticians and methodologists brings extensive experience in power analysis across diverse research fields, from clinical trials to social sciences.
Specialized statisticians for each research domain
Peer-reviewed methodology with full reproducibility
Full R/Python scripts for transparency and reuse
Our experts help you determine and justify each parameter for accurate sample size calculation.
Specialized calculations for diverse research methodologies
RCT sample size, non-inferiority designs, adaptive trials, and survival analysis with time-to-event endpoints.
Cross-sectional surveys, stratified sampling, cluster sampling, and finite population correction factors.
Repeated measures, growth curve modeling, attrition adjustment, and time-point optimization.
Analysis of covariance with covariate adjustment, baseline measurements, and confounder control.
2×2, 3×2, full factorial, fractional factorial, and interaction effect detection.
Hierarchical linear models, nested designs, cross-classified models, and ICC estimation.
Sophisticated approaches for complex research scenarios
Structural equation modeling, path analysis, factor analysis, and model fit power calculations.
Bayesian power, precision-based sample size, prior specification, and posterior probability.
Sample size for classification, regression trees, neural networks, and validation metrics.
Power under MAR, MCAR, and MNAR mechanisms, multiple imputation efficiency, and pattern mixture models.
Sample size for indirect effects, bootstrapping power, and conditional process models.
Power for cumulative meta-analysis, heterogeneity detection, and publication bias assessment.