Experimental Design and the Reliability of Priming Effects: Reconsidering the "Train Wreck"
Bibliographic record
Abstract
Failures to replicate high-profile priming effects have raised questions about the reliability of priming phenomena. Studies at the discussion’s center, labeled “social priming,” have been interpreted as a specific indictment of priming that is social in nature. However, “social priming” differs from other priming effects in multiple ways. The present research examines one important difference: whether effects have been demonstrated with within- or between-subjects experimental designs. To examine the significance of this feature, we assess the reliability of four well-known priming effects from the cognitive and social psychological literatures using both between- and within-subjects designs and analyses. All four priming effects are reliable when tested using a within-subjects approach. In contrast, only one priming effect reaches that statistical threshold when using a between-subjects approach. This demonstration serves as a salient illustration of the underappreciated importance of experimental design for statistical power, generally, and for the reliability of priming effects, specifically.
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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 | 0.223 | 0.413 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".