Naïve Statistics: Intuitive Analysis of Variance
Why this work is in the frame
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Bibliographic record
Abstract
In the present study, we explored the ability of statistics-naïve students to perform an intuitive analysis of variance and compare their performance with that of more experienced statistics students. Participants were shown several sets of data that varied with respect to within group variability and/or between group variability. They were asked to rate the strength of evidence provided by each dataset in support of a hypothetical theory. Results indicate that statistics-naïve students are able to perform a rudimentary form of analysis of variance with some accuracy, demonstrating at least a partial understanding of the importance of both within and between group variability. In one instance, statistics-naïve students actually performed in a more expert-like manner than did statistics-experienced students. However, statistics-naïve students also displayed a tendency to overweigh the relative importance of evidence provided by between group variability. This tendency persisted in statistics-experienced students.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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