Non-Parametric Test for Ordered Medians: The Jonckheere Terpstra Test
Bibliographic record
Abstract
In clinical trials, sample size is usually lesser as compared to other epidemiological studies to make it more feasible and cost effective. Small sizes of such trials discourage the use of parametric test due to violation of the assumption under which they are applicable. Therefore, the use of nonparametric test is substantial in clinical trials to test two or more independent samples. The Kruskal-Wallis h test is an alternative to one-way ANOVA and can be used to identify significant differences among different populations. When we have several independent samples and assumed to be arranged orderly, Jonckheere Terpstra test is a best choice to compare population medians instead of means. For the application of Jonckheere Terpstra test the data from the study of cleaning methods for ultrasound probes are used. The Jonckheere Terpstra test is recommended over Kruskal-Wallis h test as it compares and provides significant difference between more than two population medians when they arranged in order. Therefore, the aim of this research paper was to explore the use and significance of Jonckheere-Terpstra test with the use of practical example.
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 | 0.037 | 0.276 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".