Statistical power analysis is critical for designing robust studies that can detect meaningful effects. Avoid Type II errors and ensure your research investment yields significant, publishable findings.
Our team includes PhD-level statisticians with expertise across medical, social, and behavioral sciences.
We provide comprehensive power analysis reports that meet NIH, NSF, and other funding agency requirements.
Statistical power is the probability of correctly rejecting a false null hypothesis (1-β). Higher power means greater confidence that your study will detect an effect if one truly exists in the population.
A systematic approach to determining optimal sample size for your study design.
Define effect size, alpha level (α), desired power (1-β), and study design parameters based on literature or pilot data.
Identify appropriate statistical test: t-test, ANOVA, regression, correlation, chi-square, or complex multivariate models.
Compute sample size using specialized software: G*Power, PASS, nQuery, or custom R/Python scripts.
Determine minimum detectable effect size for fixed sample sizes and explore power across parameter variations.
Comprehensive documentation including power curves, sample size tables, and methodological justification.
Methods section write-up, grant proposal integration, and response to reviewer power-related queries.
Complete statistical power solutions for every research design and analysis type.
Prospective sample size calculation based on desired power, expected effect size, significance level, and study design parameters for grant proposals and study protocols.
Determine the minimum detectable effect size given your fixed sample size, resource constraints, or existing dataset limitations.
Retrospective power calculation for completed studies, interpretation of non-significant findings, and manuscript justification.
Power for multilevel models, cluster randomized trials, repeated measures, factorial designs, and mixed-effects analyses.
Meta-analytic pooling, literature-based effect size estimation, and Cohen's benchmark application for your research domain.
Expert guidance on G*Power, PASS, nQuery, SAS Power, Stata power, R (pwr, WebPower), SPSS SamplePower, and Python (statsmodels).
Our expertise spans across all frequently used statistical procedures.
Independent samples, paired samples, and one-sample t-tests with equal or unequal variance assumptions.
One-way, two-way, repeated measures, and analysis of covariance with factorial designs.
Pearson/Spearman correlation, multiple linear regression, and logistic regression power calculations.
Chi-square tests, proportion comparisons, McNemar's test, and binomial outcome analysis.
Specialized support for sophisticated research designs and analytic approaches.
Structural equation modeling, path analysis, factor analysis, and model fit power calculations using Satorra-Saris method.
Hierarchical linear models, growth curve analysis, and cluster-randomized trial power calculations.
Indirect effect power, conditional process analysis, and interaction detection power for complex models.
Log-rank tests, Cox regression, time-to-event endpoints, and accrual time considerations.
Multivariate analysis of variance with multiple dependent variables and covariate adjustments.
Bayesian approaches to power, precision-based sample size, and prior sensitivity analysis.
TOST procedures, non-inferiority margins, and bioequivalence study power calculations.
Power under MAR/MCAR mechanisms, multiple imputation efficiency, and attrition adjustments.