Calculate sample size, power, and minimum detectable effect for study designs.
npx skills add https://github.com/dralkh/seerai --skill statistical-power
Please help me install this Agent Skill. Skill: statistical-power Source: https://github.com/dralkh/seerai/tree/main/skills/statistical-power Command: npx skills add https://github.com/dralkh/seerai --skill statistical-power
This Skill helps you justify how many subjects, samples, or replicates you need before collecting data, so you can avoid underpowered studies, wasted resources, and weak claims.
Ask the skill to calculate the sample size or power for your planned study using your effect size, alpha level, target power, and design details.
High-intent search queries and answers about installing and using this skill.
Calculate sample size by providing your expected effect size, alpha level, target power, and design details. This Skill computes requirements for t-tests, ANOVA, proportions, correlations, chi-square, and regression using closed-form formulas and Monte Carlo validation.
Yes, you can estimate power for mixed models and clustered trials using simulation-based power analysis. This Skill applies Monte Carlo validation to complex studies including logistic regression, Poisson regression, survival analysis, mediation, and interaction-heavy designs.
You need effect-size inputs, alpha and target power settings, and planned analysis details to run a power analysis. The Skill uses these parameters to account for dropout, allocation ratios, and multiplicity in your study design.
Yes, this tool supports attrition adjustments and dropout accounting during sample-size estimation. It incorporates allocation ratios and multiplicity into the Monte Carlo validation to ensure your power analysis matches the planned study design.
The best way to plan sample size for grants and IRB protocols is to run a priori estimates, generate power curves, and conduct sensitivity analyses. This Skill produces minimum detectable effect calculations and study-planning support for preregistrations.
Use simulation-based power analysis for complex designs like clustered trials, mixed models, and survival analysis where closed-form formulas fall short. Standard designs such as two-sample t-tests, paired tests, and ANOVA use efficient closed-form power calculations instead.
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