Gaining confidence with intervals: practical guidelines, advices and tricks of the trade to face real-life situations.
Bibliographic record
Abstract
Confidence intervals and measures of effect size are gradually becoming the standard way of reporting the results of statistical analyses in research articles, used instead of or in addition to p values. However, this shift in research practices barely affected teaching practices up to now. This paper is the third of a series written to serve as a general reference on the use of confidence intervals in quantitative social sciences. Its purpose is to provide guidelines, advices and useful tricks of the trade that will allow readers (a) to face most of the statistical problems emerging in real-life research settings and (b) to improve their understanding of confidence intervals and answer more efficiently their questions of interest. The first part of the article briefly introduces the basic elements of an approach based on confidence intervals: Calculations, interpretation, and hypothesis testing. The second part is an attempt to present some of the most important (but sometimes neglected) advanced issues concerning confidence intervals: Graphic representations, complex distributions, national surveys, the larger family of interval statistics (e.g., prediction intervals), and the Bayesian approach to probabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".