Does it handle multiple comparisons?

Yes, the skill includes principles for adjusting alpha levels when running multiple tests to ensure the statistical validity of findings.

How does this skill help with writing research papers?

It provides standardized templates and guidelines for reporting statistical data such as mean, standard deviation, and test statistics according to established scientific standards.

Can it distinguish between statistical and practical significance?

Absolutely. It prioritizes effect sizes and confidence intervals over p-values to help users understand the real-world impact of their data rather than just mathematical significance.

Who is this skill for?

While designed for researchers and scientists, it is valuable for any data-driven role—including analysts and engineers—that requires accurate interpretation of experimental metrics.

Statistical Results Interpretation

Name: Statistical Results Interpretation
Author: astoreyai

byastoreyai

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データサイエンスとML

Interprets and reports complex statistical findings with high precision, focusing on effect sizes, confidence intervals, and p-value accuracy.

概要

This skill empowers Claude to act as a rigorous statistical consultant, ensuring that research results are communicated accurately and comprehensively. It guides the model to look beyond simple p-values, emphasizing the importance of effect sizes and confidence intervals to provide a true picture of data significance and practical importance. It is an essential tool for researchers and data scientists who need to draft high-quality 'Results' sections, avoid common statistical fallacies, and maintain rigorous scientific standards in their documentation and analysis workflows.

主な機能

Standardized templates for reporting test statistics and descriptive data
2 GitHub stars
Automated reporting of 95% confidence intervals
Guidance on avoiding common statistical misinterpretations
Alpha adjustment support for multiple comparison scenarios
Contextual interpretation of p-values and effect sizes

ユースケース

Drafting the Results and Discussion sections of scientific manuscripts
Validating the statistical rigor of internal data analysis reports
Explaining complex data trends to stakeholders with appropriate nuance and precision

概要

主な機能

Standardized templates for reporting test statistics and descriptive data
2 GitHub stars
Automated reporting of 95% confidence intervals
Guidance on avoiding common statistical misinterpretations
Alpha adjustment support for multiple comparison scenarios
Contextual interpretation of p-values and effect sizes

ユースケース

Drafting the Results and Discussion sections of scientific manuscripts
Validating the statistical rigor of internal data analysis reports
Explaining complex data trends to stakeholders with appropriate nuance and precision