Beyond the traditional simulation design for evaluating type 1 error rate: from ‘theoretical’ to ‘empirical’ null
Bibliographic record
Abstract
Abstract When evaluating a newly developed statistical test, the first step is to check its type 1 error (TIE) control using simulations. This is often achieved by the standard simulation design S0 under the so-called ‘theoretical’ null of no association. In practice, whole-genome association analyses scan through a large number of genetic markers ( G s) for the ones associated with an outcome of interest ( Y ), where Y comes from an unknown alternative while the majority of G s are not associated with Y , that is under the ‘empirical’ null. This reality can be better represented by two other simulation designs, where design S1.1 simulates Y from an alternative model based on G then evaluates its association with independently generated G new , while design S1.2 evaluates the association between permutated Y perm and G. More than a decade ago, Efron (2004) has noted the important distinction between the ‘theoretical’ and ‘empirical’ null in false discovery rate control. Using scale tests for variance heterogeneity and location tests of interaction effect as two examples, here we show that not all null simulation designs are equal. In examining the accuracy of a likelihood ratio test, while simulation design S0 shows the method has the correct T1E control, designs S1.1 and S1.2 suggest otherwise with empirical T1E values of 0.07 for the 0.05 nominal level. And the inflation becomes more severe at the tail and does not diminish as sample size increases. This is an important observation that calls for new practices for methods evaluation and interpretation of T1E control.
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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 | 0.323 | 0.665 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".