Bibliographic record
Abstract
Power analysis is often used to interpret nonsignificant results (NS). It is a 2-step process: Step 1: Calculate the power of the NS test. Step 2: Use the calculated power to decide whether the NS occurred because (a) the phenomenon was not present or (b) the phenomenon was present but the test had insufficient power to detect it. If power is high, one accepts: (a) the phenomenon was not present. If power is low, one cannot decide between (a) and (b): the NS is not interpretable. This reasoning has minor variations to accommodate the effect size selected for the power analysis. Lenth (2007) argued against using power to interpret NS. Lenth began with an incorrect definition of power. Power is the probability of rejecting the null when the null is false. Power is a conditional probability. The condition is “when the null is false.” Lenth (2007) defined power as “the probability of obtaining a statistically significant result.” This is not a conditional probability and is not correct. Using Lenth's phrasing, power, correctly defined, would be “the probability of obtaining a statistically significant result when the null is false.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.077 | 0.437 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.034 | 0.058 |
| Insufficient payload (model declined to judge) | 0.013 | 0.017 |
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".