Communicating Results, Limitations, Ethics, and Accessibility

Upon completing this topic

You will be able to communicate statistical results alongside effect, uncertainty, design, and limitations. You will record sources, rights, ethics, and accessibility not as separate embellishments, but as integral parts of the result communication.

Results extend beyond numbers

Results must include estimates, uncertainty, the unit of analysis, the model used, and the data scope. Do not draw conclusions from a single p-value or performance score; instead, separate what the research question can and cannot say.

Limitations are not declarations of failure

Limitations should specify constraints regarding sample, measurement, missingness, design, model, and extrapolation. Rather than ending with "further research is needed," specify which inputs or designs were insufficient. Do not treat relationships in observational data as causal, and distinguish biological significance from statistical significance.

Rights and attribution

Copyright distinguishes between ideas/facts and expression, but does not substitute for legal judgment on individual content. When using Creative Commons materials, record license conditions, attribution, links, and whether changes were made; do not assume third-party rights are automatically resolved.

For open data, provide accession numbers and official citations, and separately verify redistributability. Do not reproduce expressions, figures, or examples whose rights are unverified; instead, use your own explanations and official links.

Accessible communication

Provide titles, axes, units, and alt text for tables and figures; do not convey meaning through color alone. Include language tags and key comments in code, and describe results in text so they do not rely solely on visual materials. When technical terms first appear, connect their research meaning to their data meaning.

Result Communication Template

연구 질문: 무엇을 어떤 단위에서 비교했는가
주요 결과: 효과의 방향·크기·불확실성
분석 설계: 독립 단위·모델·다중성 기준
한계: 측정·결측·모델·외삽 경계
provenance: 데이터·코드·환경·출처
권리·윤리: 재배포·privacy·사용 제한
접근성: 표·그림·코드의 대체 설명

This template is not intended to lengthen results, but to provide a structure that allows readers to verify the boundary between calculation and interpretation. Report unfavorable results or failed diagnostics at the same level of detail.

Ethics and privacy

Do not decide on educational reuse based solely on open access status for data that may contain personal or sensitive biological information. Verify the scope of approval, de-identification, re-identification risk, and purpose of use. This content does not substitute for legal or clinical judgment; in cases of uncertainty, use synthetic data and official access procedures instead of original data.

Key Takeaways

• Communicate results alongside effect, uncertainty, design, and limitations.
• Statistical significance differs from biological significance or causation.
• Distinguish rights, citation, attribution, and redistribution.
• Accessibility is a verification condition that allows more readers to review results.

Next Steps

This module closes the scope for the KO draft of BioStatPy 33. Proceed to KO final freeze, hashing, EN/JA generation, and cross-language final review.

References

• U.S. Copyright Office FAQ: https://www.copyright.gov/help/faq/faq-protect.html
• Creative Commons licenses: https://creativecommons.org/share-your-work/cclicenses/
• ASA p-value statement: https://www.amstat.org/asa/files/pdfs/p-valuestatement.pdf
• ASA GAISE Reports: https://www.amstat.org/education/guidelines-for-assessment-and-instruction-in-statistics-education-%28gaise%29-reports

The communication structure and checklist in this module were written independently by BioStatPy.