Indiscriminate Responding Can Increase Effect Sizes for Clinical Phenomena in Nonclinical Populations: A Cautionary Note
Bibliographic record
Abstract
Indiscriminate (i.e., carless, random, insufficient effort) responses, commonly believed to weaken effect sizes and produce Type II errors, can inflate effect sizes and potentially produce Type I errors where a supposedly significant result is actually artifactual. We demonstrate how indiscriminate responses can produce spuriously high correlations in depression and hopelessness data in a nonclinical population (i.e., undergraduates), how this inflation occurs, where this misrepresentation is likely to happen, and how to guard against it. Although previous researchers have succeeded in showing this effect with samples of entirely simulated data, this study is the first to our knowledge to show that indiscriminate responding causes Type I errors in observed data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.356 | 0.700 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".