Comparison of Test Statistics for Mean Difference Testing Between Two Independent Populations
Bibliographic record
Abstract
The purpose of the article is to evaluate the efficiency of seven test statistics for mean difference testing between two independent populations. The evaluation was based on the probability of type I error and power of the test at 0.05 significance level under population distributions assumed to be normal, exponential, log-normal, gamma, and Laplace with equal sample sizes, and both equal and unequal variances. The results showed that for equal variance, the test statistics with the highest testing power controlled the probability of type I error were Z-test for normal and exponential distributions, Welch based on rank test (WBR) for log-normal and gamma distributions, and Mann-Whitney U test (MWU) for Laplace distribution. For unequal variance, Z-test was more efficient under normal, exponential, log-normal, and gamma distributions, while WBR was appropriate for Laplace distribution.
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How this classification was reachedexpand
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".