Sample Size Power Analysis Skill

    2

    Calculate and justify statistically significant sample sizes for surveys, experiments, and complex regressions.

    Free

    1 installSecurity scanned

    Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+22 more

    sample-size-power-analysis-skill

    Example session with this skill installed

    Use $sample-size-power-analysis to calculate the required N for a multiple regression with 6 predictors, alpha 0.05, and power 0.80, then write the justification paragraph.

    • Read your context and instructions
    • Compiled the sample-size-power-analysis-

    I have calculated the required sample size for your multiple regression model and drafted a formal justification paragraph based on established statistical standards.

    Power Analysis Report

    ParameterValueNote
    Test FamilyMultiple Regression (Fixed Model)
    Number of Predictors6
    Effect Size ($f^2$)0.15Medium effect (Cohen)
    Alpha ($\alpha$)0.05Type I error rate
    Power ($1-\beta$)0.80Standard threshold
    Required N98Based on Green (1991) / G*Power

    Sample Size Justification

    A power analysis was conducted to determine the minimum sample size required for a multiple regression analysis with 6 predictors. Following the heuristic recommendations of Green (1991), which suggests a minimum N > 50 + 8k (where k is the number of predictors) for testing individual predictors, a sample of 98 participants is required. Specifically, assuming a medium effect size ($f^2$ = 0.15), an alpha of 0.05, and a power of 0.80, the calculation indicates that 98 participants are necessary to reliably detect significant effects. To account for a projected 10% attrition rate, the study aims to recruit at least 110 participants.

    Next steps

    1. Install the requirements.txt dependencies to run the local sample_size.py script for variations.
    2. Adjust the effect size parameter if your pilot data suggests a smaller anticipated effect.
    3. Include the justification paragraph in the "Participants" or "Methodology" section of your manuscript.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Calculating sample size manually leads to underpowered studies and rejected papers. This skill provides the mathematical rigor needed to justify participant counts for surveys and experiments using established statistical frameworks.

    What it does

    • Power Analysis determines the relationship between effect size, alpha, power, and sample size to solve for the missing variable.
    • Multi-Method Support covers t-tests, ANOVA, correlation, chi-square, and multiple regression using Green (1991) standards.
    • Complex Modeling calculates requirements for EFA/CFA via Comrey and Lee, as well as SEM and mediation analysis.
    • Survey Sampling applies Cochran and Krejcie & Morgan formulas for descriptive study population estimates.
    • Justification Writing generates formal methodology paragraphs with authoritative citations for peer-reviewed journals.
    • Sensitivity Analysis performs post-hoc checks to identify the smallest effect size a study can reliably detect.

    How it works

    1. Identify Analysis Type by specifying your study design like a two-group experiment or a 5-predictor regression.
    2. Define Parameters by providing known values for power, significance levels, or expected effect sizes from literature.
    3. Execute Calculation using the validated Python script to generate precise N requirements.
    4. Draft Justification using templates to produce a report ready for an Ethics Committee or a manuscript.

    Frameworks & tools

    The skill includes a Python-based command-line calculator validated against G*Power benchmarks. It utilizes methodology from Cochran, Green (1991), Comrey & Lee, and Fritz & MacKinnon.

    Why this beats prompting it yourself

    LLMs often hallucinate statistical formulas or confuse sample size conventions between different test families. This skill uses a validated Python engine and a library of 11 methodology documents to ensure your N justification is mathematically sound and citeable.

    Use cases

    • Determine the responses needed for a descriptive survey of a specific population.
    • Calculate sample size for a multiple regression model with a set number of predictors.
    • Write a formal sample size justification for a grant proposal or IRB submission.
    • Perform a sensitivity analysis to see if a completed study was sufficiently powered.

    Known limitations

    The Python calculator requires a local environment with requirements.txt installed. Users must have a basic understanding of their intended statistical test family to select the right mode.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

    Reviews

    No reviews yet

    Be one of the first to try it. Every listed skill passes our trust checks below.

    Security scanned

    Passed our 8-point scan before listing

    1 install

    Downloaded by developers to date

    Free forever

    No account required to browse

    Trust & safety

    Security scanned

    Verified clean 12 days ago

    • Free to download with an account

    Listed12 days ago

    What's inside

    Frequently Asked Questions