Type I Error Inflation in the Presence of a Ceiling Effect
Bibliographic record
Abstract
Many variables in biomedical research (e.g., indices of health status) are measured with ceiling effects, in which a substantial number of subjects attain the highest possible scale value because the scale only discriminates among individuals in the low to moderate range. Furthermore, in social surveys, variables such as income and alcohol consumption may be subject to ceiling effects to protect the privacy and identity of those at the upper end of the distribution for a given variable. This article shows that if one attempts to control for such a variable using ordinary linear regression, and then test another independent variable that is actually unrelated to the outcome, the result can be an increase in the rate of Type I Error (false significance). We present simulations in which standard tests conducted at the 5%% significance level actually have the Type I error rates approaching 100%% for large samples. Statistical solutions are explored, but the best recommendation is to construct scales that are not subject to ceiling effects.
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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 | 0.285 | 0.652 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".