Time for some a priori thinking about post hoc testing
Bibliographic record
Abstract
Researchers are commonly in a situation, often after an experiment, where they want to compare the central tendency of some measure across a number of groups. If the number of groups is simply 2, then there is little controversy as to the appropriate analysis, with normally a t-test or a nonparametric equivalent being adopted. If the number of groups is greater than 2, most elementary statistical textbooks suggest performing an analysis of variance (ANOVA) to test the null hypothesis that all the groups are the same and, if this null hypothesis is rejected, implementing some post hoc testing to identify which groups are significantly different from which other groups. However, as readers and reviewers of scientific papers in behavioral science, we have noted a great diversity of approaches when comparing more than 2 groups often with little or no justification for the adoption of a specific approach. Hence, our aim in this note is to briefly survey current practice in this regard and to provide clear guidance on how such testing might most appropriately be carried out in different instances.
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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 | 0.130 | 0.352 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.010 | 0.038 |
| Insufficient payload (model declined to judge) | 0.033 | 0.025 |
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".