Statistical Analysis Advisor

Source: https://github.com/aipoch/medical-research-skills

Statistical Analysis Advisor

Intelligent statistical test recommendation engine that guides users through selecting the right statistical methods for their data.

When to Use

• Use this skill when the task needs Recommends appropriate statistical methods (T-test vs ANOVA, etc.) based.
• Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: Recommends appropriate statistical methods (T-test vs ANOVA, etc.) based.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

See ## Prerequisites above for related details.

• Python: 3.10+. Repository baseline for current packaged skills.
• dataclasses: unspecified. Declared in requirements.txt.
• enum: unspecified. Declared in requirements.txt.

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Data Analytics/statistical-analysis-advisor"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Capabilities

1. Statistical Test Selection

   • Compares and recommends between T-test, ANOVA, Chi-square, Mann-Whitney, Kruskal-Wallis, etc.
   • Considers data type, distribution, sample size, and research question
   • Provides decision tree logic for test selection

2. Assumption Checking

   • Normality tests (Shapiro-Wilk, Kolmogorov-Smirnov)
   • Homogeneity of variance (Levene's test, Bartlett's test)
   • Independence verification
   • Outlier detection guidance

3. Power Analysis & Sample Size

   • Effect size estimation (Cohen's d, eta-squared, Cramér's V)
   • Sample size calculations for desired power
   • Post-hoc power analysis

Usage

from scripts.main import StatisticalAdvisor

advisor = StatisticalAdvisor()

# Get test recommendation
recommendation = advisor.recommend_test(
    data_type="continuous",
    groups=2,
    independent=True,
    distribution="normal"
)

# Check assumptions
assumptions = advisor.check_assumptions(
    data=[group1, group2],
    test_type="independent_ttest"
)

# Power analysis
power = advisor.calculate_power(
    effect_size=0.5,
    alpha=0.05,
    sample_size=30
)

Input Parameters

Technical Difficulty: High ⚠️

Warning: Statistical recommendations have significant implications for research validity. This skill requires human verification of all recommendations before application in published research.

References

• See references/statistical_tests_guide.md for detailed test selection criteria
• See references/assumption_tests.md for assumption checking procedures
• See references/power_analysis_guide.md for power calculation methods

Limitations

• Does not perform actual data analysis (recommendations only)
• Cannot access raw data directly
• Complex multivariate designs may require specialized consultation
• Bayesian alternatives not covered comprehensively