Quality and Analysis of Small Data Sets – A Statistical Point of View
Bibliographic record
Abstract
We are often dealing with veterinary studies with very limited number of experimental units e.g.few animals.This might be due to complicated experiments, high cost for each animal, time demanding experiments, etc.All other things being equal, when the data set is small, it is difficult to demonstrate significant treatment effects.It is therefore of interest to improve the quality of data in order to demonstrate significant effects.On the other hand the choice of experimental design is also very important.Furthermore, the choice of analytical methods might also have some influence on whether or not an effect can be seen.Examples of small data sets will be given, illustrating the effect of high data quality.Different experimental designs will be discussed and the influence will be illustrated.Choosing exact methods for the statistical analysis, evaluating outliers or strange observations by influential statistics may improve the result as well.
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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 | 0.315 | 0.590 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".