AI and spotting fake studies: predatory journals and made-up stats

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-builders-research-AI-and-spotting-fake-studies-r7a10-teen

1. What is the core idea behind "AI and spotting fake studies: predatory journals and made-up stats"?

   1. AI helps you sniff out predatory journals, fake citations, and made-up statistics.
   2. Always cite the original — paraphrased ideas still need credit
   3. Identify systematic bias the cleaning rules don't surface
   4. Substitute for the data steward's review

2. Which term best describes a foundational idea in "AI and spotting fake studies: predatory journals and made-up stats"?

   1. p-hacking
   2. predatory journal
   3. retracted
   4. Always cite the original — paraphrased ideas still need credit

3. A learner studying AI and spotting fake studies: predatory journals and made-up stats would need to understand which concept?

   1. predatory journal
   2. retracted
   3. p-hacking
   4. Always cite the original — paraphrased ideas still need credit

4. Which of these is directly relevant to AI and spotting fake studies: predatory journals and made-up stats?

   1. predatory journal
   2. p-hacking
   3. Always cite the original — paraphrased ideas still need credit
   4. retracted

5. Which of the following is a key point about AI and spotting fake studies: predatory journals and made-up stats?

   1. Ask AI to check if a journal is on the Beall's List of predatory pubs
   2. Ask AI to flag retraction status of a paper
   3. Ask AI to explain p-hacking and what it looks like in a study
   4. Ask AI to find the retraction notice if there is one

6. Which of these does NOT belong in a discussion of AI and spotting fake studies: predatory journals and made-up stats?

   1. Ask AI to flag retraction status of a paper
   2. Ask AI to explain p-hacking and what it looks like in a study
   3. Always cite the original — paraphrased ideas still need credit
   4. Ask AI to check if a journal is on the Beall's List of predatory pubs

7. What is the key insight about "The rule" in the context of AI and spotting fake studies: predatory journals and made-up stats?

   1. Always cite the original — paraphrased ideas still need credit
   2. Identify systematic bias the cleaning rules don't surface
   3. Big claim + tiny sample = big skepticism.
   4. Substitute for the data steward's review

8. What is the key warning about "AI as a starting point" in the context of AI and spotting fake studies: predatory journals and made-up stats?

   1. Always cite the original — paraphrased ideas still need credit
   2. Identify systematic bias the cleaning rules don't surface
   3. Substitute for the data steward's review
   4. Use AI to generate hypotheses and summarize literature — then verify with primary sources.

9. Which statement accurately describes an aspect of AI and spotting fake studies: predatory journals and made-up stats?

   1. Some 'research' is in fake academic journals, some uses bad stats, and some has been retracted.
   2. Always cite the original — paraphrased ideas still need credit
   3. Identify systematic bias the cleaning rules don't surface
   4. Substitute for the data steward's review

10. What does working with AI and spotting fake studies: predatory journals and made-up stats typically involve?

    1. Always cite the original — paraphrased ideas still need credit
    2. Find a study cited in a viral post. Ask AI to check the journal, retraction status, and sample size. Report what you find.
    3. Identify systematic bias the cleaning rules don't surface
    4. Substitute for the data steward's review

11. Which best describes the scope of "AI and spotting fake studies: predatory journals and made-up stats"?

    1. It is unrelated to research workflows
    2. It applies only to the opposite beginner tier
    3. It focuses on AI helps you sniff out predatory journals, fake citations, and made-up statistics.
    4. It was deprecated in 2024 and no longer relevant

12. Which section heading best belongs in a lesson about AI and spotting fake studies: predatory journals and made-up stats?

    1. Always cite the original — paraphrased ideas still need credit
    2. Identify systematic bias the cleaning rules don't surface
    3. Substitute for the data steward's review
    4. How to use it

13. Which section heading best belongs in a lesson about AI and spotting fake studies: predatory journals and made-up stats?

    1. Try it
    2. Always cite the original — paraphrased ideas still need credit
    3. Identify systematic bias the cleaning rules don't surface
    4. Substitute for the data steward's review

14. Which of the following is a concept covered in AI and spotting fake studies: predatory journals and made-up stats?

    1. p-hacking
    2. predatory journal
    3. retracted
    4. Always cite the original — paraphrased ideas still need credit

15. Which of the following is a concept covered in AI and spotting fake studies: predatory journals and made-up stats?

    1. predatory journal
    2. retracted
    3. p-hacking
    4. Always cite the original — paraphrased ideas still need credit