AI and Effect Size Translation: From Cohen's d to Plain English

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-research-AI-and-effect-size-translation-r13a7-creators

1. A researcher wants to tell the public about a study finding with a Cohen's d of 0.5. Why might a plain number like '0.5' be ineffective for general audiences?

   1. The decimal point confuses readers outside scientific fields
   2. Cohen's d values are only meaningful to academic audiences
   3. The number is too small to be worth reporting publicly
   4. Most people cannot interpret standardized statistical metrics without training

2. An AI system generates an analogy comparing a 0.3 Cohen's d effect to 'the height difference between a lamp post and a giraffe.' What should the AI flag about this analogy?

   1. It is too technical for general audiences
   2. It is an inappropriate use of animal comparisons
   3. It correctly represents medium effect sizes
   4. It likely overstates the practical significance of the effect

3. Which of the following is a capability that the lesson attributes to AI in effect size translation?

   1. Guaranteeing that analogies will be understood by all audiences
   2. Replacing the need for confidence intervals in research reports
   3. Determining which framing is ethically appropriate for a specific finding
   4. Generating intuitive comparisons from standardized effects

4. According to the principles in this lesson, why might the 'most quotable' analogy for an effect size be problematic?

   1. Quotable analogies tend to be memorable precisely because they exaggerate effects
   2. Quotable analogies are always inaccurate
   3. Quotable analogies require statistical training to understand
   4. Quotable analogies are usually too complex for public audiences

5. A researcher is choosing between three AI-generated analogies for the same small effect size: one comparing it to a 2-inch height difference, another to a $5 bill, and a third to 'a life-changing difference.' Which should the researcher prefer and why?

   1. All three are equally valid representations
   2. The 2-inch height difference, because it is accurate even if less compelling
   3. The life-changing difference, because it will engage audiences most effectively
   4. The $5 bill, because monetary analogies are universally understood

6. What limitation does the lesson identify regarding AI's role in selecting the best framing for research findings?

   1. AI is unable to generate multiple framings for different audiences
   2. AI cannot produce visual analogies like heights or distances
   3. AI cannot determine which framing represents the finding honestly for a specific audience
   4. AI lacks the statistical knowledge to calculate effect sizes accurately

7. Why does the lesson caution against relying on AI-generated analogies alone when reporting research?

   1. Confidence intervals are no longer considered important in research
   2. Analogies supplement but do not replace precise statistical reporting like confidence intervals
   3. Analogies confuse more than they help general audiences
   4. AI-generated analogies are always inaccurate

8. A student asks: 'If AI can generate so many good analogies, why can't I just use the best one for my paper?' What does the lesson suggest is the problem with this approach?

   1. Academic papers require precise statistical notation, not analogies
   2. The 'best' analogy (most engaging) may inflate the effect beyond what the data supports
   3. AI-generated analogies are copyrighted material
   4. The 'best' analogy will always be too technical for general audiences

9. Which of the following best describes what 'effect size' means in the context of this lesson?

   1. A label indicating whether research results are statistically significant
   2. A numerical measure of the magnitude or strength of a relationship between variables
   3. A measurement of how many participants were in a study
   4. The probability that a finding occurred by chance

10. The lesson mentions that AI can provide multiple framings for different audiences. What does 'framing' refer to in this context?

    1. Presenting the same statistical finding in ways that resonate with different groups
    2. Deciding whether to publish negative results
    3. Choosing which data to include in a study
    4. Hiding limitations in how research is described

11. Why might a researcher need different analogies for the same effect when communicating to a medical journal versus a news article?

    1. News articles require mathematical formulas instead of analogies
    2. Medical journals prohibit the use of analogies
    3. The journal audience already understands statistics and needs precision, while news readers need accessible comparisons
    4. Researchers are not allowed to simplify findings for news outlets

12. What risk does the lesson identify with using the most dramatic analogy an AI generates?

    1. It typically understates the effect size
    2. It will likely be too boring to capture attention
    3. It may mislead audiences about the practical significance of findings
    4. It will confuse readers with unfamiliar comparisons

13. In the context of this lesson, what does 'public scholarship' primarily refer to?

    1. Publishing research in open-access journals
    2. Efforts to communicate academic research findings to non-academic audiences
    3. Applying academic theories to solve real-world problems
    4. Teaching research methods to undergraduate students

14. A researcher notices that AI generated an analogy comparing a small effect to 'the difference between a penny and a dime.' Why might this be preferable to an analogy about 'a dramatic life change'?

    1. The penny/dime comparison accurately represents a small effect without overstating it
    2. Penny and dime are scientific terms that researchers recognize
    3. Dime comparisons are universally understood, while life change analogies are not
    4. Life change analogies are too abstract for any audience

15. What is one reason the lesson gives for why AI is valuable in effect size translation?

    1. AI can guarantee that generated analogies are always perfectly accurate
    2. AI eliminates the need for researchers to understand statistics
    3. AI can quickly generate multiple intuitive comparison options for consideration
    4. AI can determine which findings are important enough to publish