STATS 810 (Fall 2026), Class 1

Class web site: https://ionides.github.io/810f26

Syllabus

Prompt

How does your reputation as a responsible researcher and scholar become generated and transmitted through the academic community?

UM-GPT 5.6 Sol

Your reputation as a responsible researcher and scholar is built through repeated evidence that your work—and your conduct—can be trusted. It is then transmitted through both formal academic systems and informal professional networks.

How reputation is generated

• Quality and rigor: You use appropriate methods, document decisions, acknowledge limitations, and produce careful, reproducible scholarship.
• Research integrity: You report results honestly, manage conflicts of interest, protect participants and data, follow ethical and regulatory requirements, and correct mistakes promptly.
• Responsible authorship: You credit contributors fairly, cite sources accurately, avoid plagiarism and duplicate publication, and clarify authorship expectations early.
• Reliability and collegiality: You meet deadlines, communicate clearly, keep commitments, and treat students, staff, collaborators, and research participants respectfully.
• Transparency: You share methods, code, data, or materials when ethically and legally appropriate. Transparent corrections and well-explained negative results can strengthen—not diminish—trust.
• Contribution to the community: Mentoring, peer review, committee work, replication studies, and constructive scholarly debate all demonstrate professional responsibility.

How reputation is transmitted

• Publications and scholarly outputs: Articles, books, datasets, software, exhibitions, patents, and other products create a visible record of your work.
• Peer review and editorial processes: Reviewers and editors form judgments about your rigor, fairness, responsiveness, and professionalism.
• Citations and use by others: References, replications, clinical or policy applications, and adoption of your tools signal influence—though citation counts alone do not establish quality or integrity.
• Conferences and seminars: Presentations, questions, and informal conversations allow colleagues to observe both your expertise and your conduct.
• Mentors, collaborators, and trainees: Letters of recommendation, referrals, introductions, and word of mouth carry reputational information across institutions and disciplines.
• Institutional signals: Appointments, promotions, grants, awards, leadership roles, and professional memberships can validate reputation, although they are imperfect measures.
• Digital presence: Professional profiles, preprints, repositories, social media, and public scholarship make your record more visible and searchable.

Reputation is therefore cumulative, social, and contextual. A strong reputation usually develops slowly from consistent behavior, but serious misconduct can damage it quickly. It is also important to recognize that academic reputations are not formed in a perfectly fair system: prestige, network access, disciplinary norms, and structural bias can affect whose work is noticed and believed.

The most durable strategy is not simply “reputation management,” but maintaining a verifiable record of rigorous work, ethical judgment, fair credit, respectful relationships, and accountability when errors occur. In academia, people remember excellent findings—but they also remember who was trustworthy to work with.

Prompt

Summarize this in 3 sentences.

ChatGPT 5.6

A responsible scholarly reputation is built through rigorous, ethical, transparent research; fair authorship; reliability; respectful collaboration; and meaningful service to the academic community. It spreads through publications, peer review, citations, conferences, recommendations, institutional recognition, professional networks, and digital platforms. Because reputation is cumulative but shaped by unequal systems, the best approach is to consistently produce trustworthy work, treat others fairly, and take responsibility for mistakes.

Conclusion:

Current AI gives good general advice. But it is less clear how to obtain from the AI good concrete advice on how to make practical decisions as a PhD student in Statistics at UM. AI can help your thinking, but on important matters you should not let it replace your thinking.

• How should I balance time between the many demands on a Statistics PhD student?

• How do I get good advice on professional decisions?

Class format:

• 2 minutes for a question or comment relevant to the class topic.

• For this class,

  • Say your name (as you wish to be called) and your home town.

  • Summarize what you use AI for.

  • What do you view as the main dangers and opportunites for AI in the context of education, research and practice in the field of statistics?