Sequential Tests Based on F-Distribution for Detecting Active Effects in Unreplicated Two-Level Factorial Designs
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it