Average Power: A Cautionary Note
Bibliographic record
Abstract
Replication is an important contemporary issue in psychological research, and there is great interest in ways of assessing replicability, in particular, retrospectively via prior studies. The average power of a set of prior studies is a quantity that has attracted considerable attention for this purpose, and techniques to estimate this quantity via a meta-analytic approach have recently been proposed. In this article, we have two aims. First, we clarify the nature of average power and its implications for replicability. We explain that average power is not relevant to the replicability of actual prospective replication studies. Instead, it relates to efforts in the history of science to catalogue the power of prior studies. Second, we evaluate the statistical properties of point estimates and interval estimates of average power obtained via the meta-analytic approach. We find that point estimates of average power are too variable and inaccurate for use in application. We also find that the width of interval estimates of average power depends on the corresponding point estimates; consequently, the width of an interval estimate of average power cannot serve as an independent measure of the precision of the point estimate. Our findings resolve a seeming puzzle posed by three estimates of the average power of the power-posing literature obtained via the meta-analytic approach.
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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 | 0.261 | 0.614 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.017 | 0.007 |
| Research integrity | 0.015 | 0.058 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".