Is twenty plenty? Tracking the stability of basic pointing kinematic measures over trials and across vision conditions
Bibliographic record
Abstract
Although the number of participants required for a study can be readily determined via power analyses, the number of trials completed in any given condition is typically chosen out of convenience. One of the presumptions of the central-limit theorem is that if sufficient trials have been collected, then the sample mean should approximate the population mean. The purpose of the current study was to evaluate the number of trials required for stable estimates of of basic kinematic variables commonly used for pointing tasks across basic, full-vision and no-vision conditions. Ten participants completed twenty pointing movements to each of three visual targets (27, 30, and 33 cm amplitude) in both full-vision, and no-vision viewing conditions. Running, cumulative means were computed on a trial-by-trial basis for the following kinematic measures: reaction time, time-to-peak-limb velocity, time-after-peak-limb velocity, peak-limb velocity, movement time, and constant error. These running estimates were considered stable when they entered and remained within a +/- 5 % Z-score bandwidth around the final, cumulative estimate. Across all measures and conditions, between 14.5 (+/- 1.3) and 15.4 (+/- 1.3) trials were required to achieve cumulative mean stability. Therefore, if 18 or more are to be collected for a given vision condition, one can be reasonably confident that their mean data are representative of the true condition parameters. That is, under the current experimental conditions, twenty trials is plenty.Acknowledgments: Acknowledgements: Natural Sciences and Engineering Research Council of Canada (NSERC)
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".