La prueba de significancia de la hipótesis nula y la dicotomización del valor p: <i>Errare Humanum Est</i>
Bibliographic record
Abstract
Decision-making in healthcare is complex and needs to be based on the best scientific evidence. In this process, information derived from statistical analysis of data is crucial, which can be developed from either frequentist or Bayesian perspectives. When it comes to the frequentist field, the null hypothesis significance test (NHST) and its p-value is one of the most widely used techniques in different disciplines. However, NHST has been subjected to questioning from different academic points of view, which has led to it being considered as one of the causes of the so-called replicability crisis in science. In this review article, we provide a brief historical account of its development, summarize the underlying methods, describe some controversies and limitations, address misuse and misinterpretation, and finally give some scopes and reflections in the context of biomedical research.
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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 | 0.055 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".