Time for some a priori thinking about post hoc testing
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
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.
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 | 0.002 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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