AI Negative-Results Publication Narrative: Drafting Null-Finding Manuscript Summaries

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-research-AI-and-negative-results-publication-narrative-r8a3-creators

1. According to the capabilities described, what can AI do effectively when drafting negative-results narratives?

   1. Guarantee that the manuscript will pass peer review
   2. Restructure raw notes into a coherent, decision-ready summary
   3. Decide whether the null finding is scientifically important enough to publish
   4. Determine which co-authors should review the manuscript before submission

2. When drafting a negative-results manuscript, what should be quoted first if the study had low statistical power?

   1. The p-value from the primary analysis
   2. The achieved statistical power
   3. The effect size estimate
   4. The sample size used in the study

3. What is a key limitation of AI when drafting negative-results narratives involving stakeholder concerns?

   1. AI cannot interpret statistical output
   2. AI cannot write in academic tone
   3. AI cannot decide which stakeholders need a separate conversation before the document lands
   4. AI cannot format citations correctly

4. When concerns about a negative-results manuscript are political, ethical, or relational rather than analytical, what limitation does AI face?

   1. AI cannot generate enough text for the manuscript
   2. AI cannot read the room—these concerns require human situational awareness
   3. AI cannot access the relevant data
   4. AI cannot perform the statistical analysis correctly

5. What three components should an AI-drafted negative-results narrative summary include?

   1. A one-paragraph headline framing, three substantive points with caveats, and two explicit decisions for reviewers
   2. Statistical tables, figures, and supplemental materials
   3. Introduction, methods, and discussion sections
   4. Title, abstract, and references

6. What does AI do when it surfaces unresolved questions that inputs imply but the draft glosses over?

   1. It automatically fills in the gaps with assumptions
   2. It converts them to positive findings
   3. It helps identify gaps that require human judgment to resolve
   4. It deletes the problematic sections

7. A researcher uses AI to draft a manuscript from a study that found no statistically significant effect. The AI presents this as a confirmed null finding. What is the key concern?

   1. The study may have had low power, so presenting it as confirmed null misrepresents what was actually found
   2. The journal will reject any manuscript with negative results
   3. The manuscript will be too long
   4. The AI will have made up data

8. What type of decisions must a human reviewer make before signing off on an AI-drafted negative-results manuscript?

   1. Explicit decisions or asks that the AI cannot determine alone
   2. Which font to use for the title
   3. How many pages the manuscript should be
   4. Whether to include a table of contents

9. Why might a journal reject a negative-results manuscript that hasn't been reframed?

   1. Because without proper narrative framing, journals may view null findings as uninformative or not worth publishing
   2. Because AI cannot write negative-results manuscripts
   3. Because negative results are illegal to publish
   4. Because the journal only publishes positive findings

10. What happens if an AI-drafted negative-results manuscript treats a low-power study as a confirmed null?

    1. The manuscript will be more likely to be cited
    2. The statistical analysis will be more accurate
    3. You are publishing a different finding than you actually ran
    4. The journal will automatically accept it

11. In the context of negative-results publication, what does 'rebranding the null' mean?

    1. Giving the study a new title
    2. Restructuring a null finding so it appears meaningful or publishable without explicitly stating it found no effect
    3. Adding more co-authors
    4. Changing the author names

12. What is the purpose of including caveats in a negative-results manuscript draft?

    1. To confuse reviewers
    2. To make the manuscript longer
    3. To honestly acknowledge limitations or alternative interpretations of the null finding
    4. To hide the negative results

13. When AI drafts a negative-results narrative, what cannot it decide about the manuscript's audience?

    1. How many paragraphs to include
    2. How many words to use
    3. Which stakeholders need a separate conversation before the document lands
    4. What font size to use

14. What does the 'headline framing' component of an AI-drafted negative-results summary accomplish?

    1. It provides the exact title for the manuscript
    2. It summarizes the statistical methods
    3. It lists all co-authors
    4. It gives a one-paragraph context that frames what the study found and why it matters despite being null

15. Why is it important to explicitly state the achieved statistical power in a negative-results manuscript?

    1. Because journals require it for formatting
    2. Because it contextualizes the null finding—the study may have been underpowered to detect an effect
    3. Because power calculations are always required by ethics boards
    4. Because AI cannot calculate power