Beyond the traditional simulation design for evaluating type 1 error: from ‘theoretical’ to ‘empirical’ null
Bibliographic record
Abstract
When evaluating a new statistical test, it’s important to check the type 1 error (T1E) control, often achieved by the `theoretical' design S0. In whole-genome association analyses, people scan through large numbers of genes (Gs) for the ones associated with an outcome(Y). Y comes from an unknown alternative and is not associated with the majority of Gs, This reality can be represented by two `empirical’ designs, where S1.1 simulates Y from G then evaluates its association with independently generated Gnew; while S1.2 evaluates the association between permutated Yperm and G. Using scale tests, location tests with single and multiple Gs as examples, we show that not all designs are equal. For certain statistics, T1E inflation can only be revealed under 1empirical’ designs and doesn’t diminish as sample size increases. This important observation calls for new practices for methods evaluation and interpretation of T1E control.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.022 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".