Evaluation of several Efron bootstrap methods to estimate error measures for software metrics
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
A narrow confidence interval of a sample statistic or a model parameter implies low variability of that statistic, and permits a strong conclusion to be made about the underlying population. Conversely, the analysis should be considered inconclusive if the confidence interval is wide. Efron's (1992) bootstrap statistical analysis appears to address the fact that many statistics used in software metrics analysis do not come with theoretical formulas to allow accuracy assessment. In this paper we will present preliminary results on an empirical analysis of the reliability of several Efron nonparametric bootstrap methods in assessing the accuracy of sample statistics in the context of software metrics. In particular, we focus on the standard errors and 90% confidence intervals of five basic statistics as a tool to evaluate the Bootstrap. It was found confidence intervals for mean and median were accurately estimated, those for variance grossly under-estimated with skewness and kurtosis grossly over-estimated.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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