Statistical Power and Effect Sizes of Clinical Neuropsychology Research
Why this work is in the frame
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Bibliographic record
Abstract
Cohen, in a now classic paper on statistical power, reviewed articles in the 1960 issue of one psychology journal and determined that the majority of studies had less than a 50-50 chance of detecting an effect that truly exists in the population, and thus of obtaining statistically significant results. Such low statistical power, Cohen concluded, was largely due to inadequate sample sizes. Subsequent reviews of research published in other experimental psychology journals found similar results. We provide a statistical power analysis of clinical neuropsychological research by reviewing a representative sample of 66 articles from the Journal of Clinical and Experimental Neuropsychology, the Journal of the International Neuropsychology Society, and Neuropsychology. The results show inadequate power, similar to that for experimental research, when Cohen's criterion for effect size is used. However, the results are encouraging in also showing that the field of clinical neuropsychology deals with larger effect sizes than are usually observed in experimental psychology and that the reviewed clinical neuropsychology research does have adequate power to detect these larger effect sizes. This review also reveals a prevailing failure to heed Cohen's recommendations that researchers should routinely report a priori power analyses, effect sizes and confidence intervals, and conduct fewer statistical tests.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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