Tempered Expectations: A Tutorial for Calculating and Interpreting Prediction Intervals in the Context of Replications
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
Over the last decade, replication research in the psychological sciences has become more visible. One way that replication research can be conducted is to compare the results of the replication study with the original study to look for consistency, that is to say, to evaluate whether the original study is “replicable.” Unfortunately, many popular and readily accessible methods for ascertaining replicability, such as comparing significance levels across studies or eyeballing confidence intervals, are generally ill suited to the task of comparing results across studies. To address this issue, we present the prediction interval as a statistic that is effective for determining whether a replication study is inconsistent with the original study. We review the statistical rationale for prediction intervals, demonstrate hand calculations, and provide a walkthrough using an R package for obtaining prediction intervals for means, d values, and correlations. To aid the effective adoption of prediction intervals, we provide guidance on the correct interpretation of results when using prediction intervals in replication research.
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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 | 0.011 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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