Mixed-effects design analysis for experimental phonetics
Bibliographic record
Abstract
It is common practice in the statistical analysis of phonetic data to draw conclusions on the basis of statistical significance, often judged by the size of a p-value. While p-values reflect the probability of incorrectly concluding a null effect is real, they do not provide information about other types of error that are also important for interpreting statistical results. In particular, it is possible to fail to detect a true effect, to exaggerate the magnitude of an effect, or even to incorrectly estimate an effect's direction, resulting in erroneous and biased measures of effect size. In this technical report, we focus on three measures related to these errors. The first, power, reflects the failure to detect an effect that in fact exists. The second and third, Type M and Type S errors, measure the extent to which estimates of the magnitude and direction of an effect are inaccurate. We then provide 'design analysis' (Gelman & Carlin, 2014), using data from an experimental study on German incomplete neutralization, to illustrate how power, magnitude, and sign errors vary with sample and effect size. This case study shows how the informativity of research findings can vary substantially in ways that are not always, or even usually, apparent on the basis of a p-value alone. We conclude by repeating three recommendations for good statistical practice in phonetics from best practices widely recommended for the social and behavioral sciences: report all results; design studies which will produce high-precision estimates; and conduct direct replications of previous findings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".