AI导哈

数据与研究

statistical-power

k-dense-ai/scie…t-skills

Calculates sample sizes and statistical power for study planning. Applies when someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for complex designs — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Also handles requests that only mention an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.

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安装命令

npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill statistical-power

SKILL.md

Statistical Power & Sample Size

Overview

Power analysis plans the probability of rejecting a specified null under an assumed alternative. It answers how many independent experimental units are needed to detect a scientifically important effect, or what effects a feasible sample could detect. Choose the inferential goal first: precision, equivalence, noninferiority, or sequential monitoring need their own calculations; the bundled superiority-test helpers do not cover them.

Four quantities are locked together for any given test: sample size (n), effect size, significance level (α), and power (1 − β). For a fixed design, analysis, and nuisance parameters, fixing three permits solving for the fourth when a solution exists. Every calculation in this skill is some rearrangement of that relationship.

This skill covers the two ways to do power analysis:

  • Analytical power calculations (exact under some standard-test assumptions; asymptotic approximations for others) — see references/closed_form_recipes.md.
  • Simulation / Monte Carlo (requires a credible data-generating process and a calibrated planned analysis) — see references/simulation_based_power.md.

For choosing and converting effect sizes — usually the hardest part — see references/effect_sizes.md.

When to Use This Skill

Statistical Power & Sample Size

Overview

Power analysis plans the probability of rejecting a specified null under an assumed alternative. It answers how many independent experimental units are needed to detect a scientifically important effect, or what effects a feasible sample could detect. Choose the inferential goal first: precision, equivalence, noninferiority, or sequential monitoring need their own calculations; the bundled superiority-test helpers do not cover them.

Four quantities are locked together for any given test: sample size (n), effect size, significance level (α), and power (1 − β). For a fixed design, analysis, and nuisance parameters, fixing three permits solving for the fourth when a solution exists. Every calculation in this skill is some rearrangement of that relationship.

This skill covers the two ways to do power analysis:

  • Analytical power calculations (exact under some standard-test assumptions; asymptotic approximations for others) — see references/closed_form_recipes.md.
  • Simulation / Monte Carlo (requires a credible data-generating process and a calibrated planned analysis) — see references/simulation_based_power.md.

For choosing and converting effect sizes — usually the hardest part — see references/effect_sizes.md.

When to Use This Skill

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