Low Prevalence of A Priori Power Analyses in Motor Behavior Research
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
A priori power analyses can ensure studies are unlikely to miss interesting effects. Recent metascience has suggested that kinesiology research may be underpowered and selectively reported. Here, we examined whether power analyses are being used to ensure informative studies in motor behavior. We reviewed every article published in three motor behavior journals between January 2019 and June 2021. Power analyses were reported in 13% of studies ( k = 636) that tested a hypothesis. No study targeted the smallest effect size of interest. Most studies with a power analysis relied on estimates from previous experiments, pilot studies, or benchmarks to determine the effect size of interest. Studies without a power analysis reported support for their main hypothesis 85% of the time, while studies with a power analysis found support 76% of the time. The median sample sizes were n = 17.5 without a power analysis and n = 16 with a power analysis, suggesting the typical study design was underpowered for all but the largest plausible effect size. At present, power analyses are not being used to optimize the informativeness of motor behavior research. Adoption of this widely recommended practice may greatly enhance the credibility of the motor behavior literature.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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