The Modification and Evaluation of the Alexander-Govern Test in Terms of Power
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Bibliographic record
Abstract
<p class="zhengwen"><span lang="EN-GB">This study centres on the comparison of independent group tests in terms of power, by using parametric method, such</span><span lang="EN-GB"> as the Alexander-Govern test. The Alexander-Govern (<em>AG</em>) test uses mean as its central tendency measure. It is a better alternative compared to the Welch test, the James test and the <em>ANOVA</em>, because it produces high power and gives good control of Type I error rates for a normal data under variance heterogeneity. But this test is not robust for a non-normal data. When trimmed mean was applied on the test as its central tendency measure under non-normality, the test was only robust for two group condition, but as the number of groups increased more than two groups, the test was no more robust. As a result, a highly robust estimator known as the <em>MOM</em> estimator was applied on the test, as its central tendency measure. This test is not affected by the number of groups, but could not control Type I error rates under skewed heavy tailed distribution. In this study, the Winsorized <em>MOM</em> estimator was applied in the <em>AG</em> test, as its central tendency measure. A simulation of 5,000 data sets were generated and analysed on the test, using the <em>SAS</em> package. The result of the analysis, shows that with the pairing of unbalanced sample size of (15:15:20:30) with equal variance of (1:1:1:1) and the pairing of unbalanced sample size of (15:15:20:30) with unequal variance of (1:1:1:36) with effect size index (<em>f</em> = 0.8), the <em>AGWMOM </em>test only produced a high power value of 0.9562 and 0.8336 compared to the <em>AG </em>test, the <em>AGMOM </em>test and the <em>ANOVA </em>respectively and the test is considered to be sufficient.</span></p>
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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 | 0.002 | 0.000 |
| 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