Restriction of Range and Correlation in Outlier-Prone Distributions
Why this work is in the frame
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Bibliographic record
Abstract
Statistical theory indicates that restriction of the range of possible values of normally distributed variables, and many non-normal variables, reduces correlations in unrestricted populations. Contrary to this typical outcome, results of a simulation study show that range restriction sometimes increased the correlation between variables having outlier-prone distributions. This result occurred in the case of exponential and ex-Gaussian distributions, which are encountered in experimental studies involving response times. It did not occur in truncated versions of the same densities. Chance occurrence of outliers in contaminated-normal, or mixed-normal, distributions reduced the correlation found between samples from uncontaminated populations. Conversely, detection and downweighting of outliers increased the magnitude of sample correlations, and a similar result occurred for many other outlier-prone distributions. Practical implications of these findings are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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