Sequential Tests Based on F-Distribution for Detecting Active Effects in Unreplicated Two-Level Factorial Designs
Bibliographic record
Abstract
This paper introduces a new methodology for detecting active effects in unreplicated two-level factorial experiments, which are of great importance in many scientific and practical fields. The proposed method aims to enhance the reliability and accuracy of detecting active effects compared to the popular method introduced by Lenth (1989). The new approach utilizes the F-distribution for significance testing, and it eliminates the needs of estimating the error variance or creating new critical value tables. A comprehensive simulation study was conducted using the Monte Carlo simulation method to evaluate the performance of the proposed method compared to Lenth's method in terms of the size and power of the test under different conditions. The results demonstrated the superiority of the proposed method. Additionally, three practical applications were analyzed using both methods to illustrate the practical effectiveness of the new approach. The simplicity and robustness of the proposed method make it a practical and effective method and a good choice for the analysis of unreplicated two-level factorial experiments.
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 | 0.030 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".