Fuzzy Method for Multiple Hypotheses Testing Procedure
Bibliographic record
Abstract
Abstract Multiple hypotheses testing is a procedure for testing many hypotheses simultaneously which can control familywise error rate (FWER). The primary method was proposed by Bonferroni and it is the most popular among all procedures for controlling FWER. Many multiple hypothesis tests have been developed by changing a constant in each testing step including the Hochberg method and Bonferroni–Sidak method. These multiple hypotheses modification methods are more powerful than the classic Bonferroni’s method and they still focus on controlling the FWER. However, there is an alternative method to improve the multiple hypotheses testing procedures without changing their critical value sets, which is called a fuzzy method. In this study, the fuzzy method will be applied to the Bonferroni method, the Hochberg method and the Bonferroni–Sidak method. The power of the original multiple hypotheses testing method and the fuzzy multiple hypotheses testing method are compared by simulation study using the R program.
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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 | 0.032 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.009 |
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 source (direct Gemma or distilled Codex), 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".