Controlling the rate of Type I error over a large set of statistical tests
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When many tests of significance are examined in a research investigation with procedures that limit the probability of making at least one Type I error--the so-called familywise techniques of control--the likelihood of detecting effects can be very low. That is, when familywise error controlling methods are adopted to assess statistical significance, the size of the critical value that must be exceeded in order to obtain statistical significance can be extremely large when the number of tests to be examined is also very large. In our investigation we examined three methods for increasing the sensitivity to detect effects when family size is large: the false discovery rate of error control presented by Benjamini and Hochberg (1995), a modified false discovery rate presented by Benjamini and Hochberg (2000) which estimates the number of true null hypotheses prior to adopting false discovery rate control, and a familywise method modified to control the probability of committing two or more Type I errors in the family of tests examined--not one, as is the case with the usual familywise techniques. Our results indicated that the level of significance for the two or more familywise method of Type I error control varied with the testing scenario and needed to be set on occasion at values in excess of 0.15 in order to control the two or more rate at a reasonable value of 0.01. In addition, the false discovery rate methods typically resulted in substantially greater power to detect non-null effects even though their levels of significance were set at the standard 0.05 value. Accordingly, we recommend the Benjamini and Hochberg (1995, 2000) methods of Type I error control when the number of tests in the family is large.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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