Summary Plots With Adjusted Error Bars: The <i>superb</i> Framework With an Implementation in R
Bibliographic record
Abstract
Plotting the data of an experiment allows researchers to illustrate the main results of a study, show effect sizes, compare conditions, and guide interpretations. To achieve all this, it is necessary to show point estimates of the results and their precision using error bars. Often, and potentially unbeknownst to them, researchers use a type of error bars—the confidence intervals—that convey limited information. For instance, confidence intervals do not allow comparing results (a) between groups, (b) between repeated measures, (c) when participants are sampled in clusters, and (d) when the population size is finite. The use of such stand-alone error bars can lead to discrepancies between the plot’s display and the conclusions derived from statistical tests. To overcome this problem, we propose to generalize the precision of the results (the confidence intervals) by adjusting them so that they take into account the experimental design and the sampling methodology. Unfortunately, most software dedicated to statistical analyses do not offer options to adjust error bars. As a solution, we developed an open-access, open-source library for R— superb—that allows users to create summary plots with easily adjusted error bars.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.313 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.168 | 0.040 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".