Detecting a lack of association: An equivalence testing approach
Why this work is in the frame
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Bibliographic record
Abstract
Researchers often test for a lack of association between variables. A lack of association is usually established by demonstrating a non-significant relationship with a traditional test (e.g., Pearson's r). However, for logical as well as statistical reasons, such conclusions are problematic. In this paper, we discuss and compare the empirical Type I error and power rates of three lack of association tests. The results indicate that large, sometimes very large, sample sizes are required for the test statistics to be appropriate. What is especially problematic is that the required sample sizes may exceed what is practically feasible for the conditions that are expected to be common among researchers in psychology. This paper highlights the importance of using available lack of association tests, instead of traditional tests of association, for demonstrating the independence of variables, and qualifies the conditions under which these tests are appropriate.
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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 | 0.007 | 0.203 |
| 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 | 0.001 |
| 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