Credibility of results in emotion science: a <i>Z</i> -curve analysis of results in the journals <i>Cognition & Emotion</i> and <i>Emotion</i>
Bibliographic record
Abstract
Failed replication attempts have raised concerns over the prevalence of publication bias and false positive results in the psychological literature. Using a sample of 65,970 test statistics from Cognition & Emotion and Emotion, this article assesses the credibility of results in emotional research. All test statistics were converted to z-scores and analysed with Z-curve. A Z-curve analysis provides information about the amount of selection bias, the expected replication rate and the false positive risk. Lastly, Z-curve is used to determine an alpha level that lessens the false positive risk without unnecessary loss of power. The results show evidence of selection bias in emotional research, but trend analyses showed a decrease over time. Based on the z-curve estimates, we predict a 15% and 70% success rate in replication studies. Therefore, replication studies should increase sample sizes to avoid type-II errors. The risk of false positives with the traditional alpha level of 5% is between 5% and 33%. Lowering alpha to 1% is sufficient to reduce the false positive risk to less than 5%. In sum, our findings may alleviate concerns about high false positive rates among emotional researchers. However, selection bias and low power remain challenges to be addressed.
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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 | 0.184 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.021 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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; both teacher heads 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".