Bibliographic record
Abstract
Dear Editor-in-Chief, In a recent article by Schoenfeld et al. (1) about the effect of training volume on muscle growth and strength, the authors justified a sample size of n = 36 by “a priori power analysis in G*power.” This power analysis used “a target effect size (ES) of f = 0.25, alpha of 0.05 and power of 0.80,” with “group (one, three, or five sets) as the factor” and “baseline value as a covariate.” Although the authors provided the said information, they did not provide sufficient information to replicate their calculations. More specifically, the numerator df and number of groups were missing. When replicating this power analysis using G*power and the same values, assuming Numerator df = 2 (3(factor levels) − 1) and Number of Groups = 3, I obtained a different sample size of n = 158. In addition to this finding, I have checked what the correct original power estimate would have been, using the sample size of n = 36 and the preexperiment chosen effect size of f = 0.25, and found it to be 0.23. Therefore, this study design by Schoenfeld et al. (1) is deemed to be underpowered for the effect size chosen (2). Although some methodologists and statisticians advise to abandon low-power studies, and many ethics review boards find them unethical (3), low-powered trials should be published irrespective of their results, thereby becoming available for meta-analysis (4,5). However, these low-powered studies must report their methods and results properly to avoid misinterpretation (2), which was not the case in this article. In addition, even if the a priori power analysis was correct and the sample of n = 36 was sufficient for a power value of 0.8, the number of participants in the final analysis was smaller than 36 (n = 34) (1). As a result, power analysis of this final sample size, with the predetermined effect size of f = 0.25, results in even lower value of power, 0.218. Underpowered study designs undermine the purpose of scientific research, as they decrease the chance to detect a true effect (i.e., they have high risk of type II error) (6). In addition, underpowered studies have a substantial risk to report inflated effect sizes because of the sampling distribution of the means having a high variance compared with appropriately powered studies (7). Therefore, for a study design with a power of 0.23, the risk of reporting substantially inflated effect sizes should be considered relatively high. Eliran Mizelman EM-SportScience, Vancouver, BC, CANADA Sports Analytics Group and Department of Biomedical Physiology and Kinesiology Simon Fraser University, Burnaby, BC, CANADA
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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 | 0.283 | 0.656 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".