More Voodoo Correlations: When Average-Based Measures Inflate Correlations
Why this work is in the frame
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Bibliographic record
Abstract
A Monte-Carlo simulation was conducted to assess the extent that a correlation estimate can be inflated when an average-based measure is used in a commonly employed correlational design. The results from the simulation reveal that the inflation of the correlation estimate can be substantial, up to 76%. Additionally, data was re-analyzed from two previously published studies to determine the extent that the correlation estimate was inflated due to the use of an averaged based measure. The re-analyses reveal that correlation estimates had been inflated by just over 50% in both studies. Although these findings are disconcerting, we are somewhat comforted by the fact that there is a simple and easy analysis that can be employed to prevent the inflation of the correlation estimate that we have simulated and observed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it