On the reproducibility of power analyses in motor behavior research
Bibliographic record
Abstract
Recent metascience suggests that motor behavior research may be underpowered, on average. Researchers can perform a priori power analyses to ensure adequately powered studies. However, there are common pitfalls that can result in underestimating the required sample size for a given design and effect size of interest. Critical evaluation of power analyses requires successful analysis reproduction, which is conditional on the reporting of sufficient information. Here, we attempted to reproduce every power analysis reported in articles (k = 84/635) in three motor behavior journals between January 2019 and June 2021. We reproduced 7% of analyses using the reported information, which increased to 43% when we assumed plausible values for missing parameters. Among studies that reported sufficient information to evaluate, 63% reported using the same statistical test in the power analysis as in the study itself, and in 77%, the test addressed at least one of the identified hypotheses. Overall, power analyses were not commonly reported with sufficient information to ensure reproducibility. A nontrivial number of power analyses were also affected by common pitfalls. There is substantial opportunity to address the issue of underpowered research in motor behavior by increasing adoption of power analyses and ensuring reproducible reporting practices.
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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 | 0.008 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 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".