Verifying a dominant cause of output variation
Bibliographic record
Abstract
Finding the dominant cause(s) of variation in process improvement projects is an important task. Before trying to reduce variation in the dominant cause or mitigate the effect of variation in the dominant cause to reduce output variation, it is strongly recommended that we verify we have identified the true (dominant) cause. This article is about how best to verify we have correctly identified a dominant cause, as the existing literature does not properly answer this question. Although it may seem that a randomized controlled experiment is sufficient for this purpose, we show that experimental studies alone cannot provide all the required information. An experiment identifies whether a suspect is a cause of variation; however, we also require additional information (i.e., from observational studies) to determine whether it is dominant and not just significant. This article lists some viable composite study designs, assesses their relative merits, and recommends proper sample sizes. We also investigate how to systematically conduct a verification study in the era of smart manufacturing. Moreover, we provide a tangible example to illustrate our proposed procedure.
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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 | 0.143 | 0.321 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".