Conditional Probabilities of Rejecting <i>H</i> <sub>0</sub> by Pooled and Separate-Variances <i>t</i> Tests Given Heterogeneity of Sample Variances
Bibliographic record
Abstract
It is known that the Type I error probability of the Student t test is spuriously elevated or depressed by unequal variances combined with unequal sample sizes and that the Welch separate-variances version of the t test usually eliminates these effects. The present study found conditional probabilities of rejecting the null hypothesis, for both significance tests, given various conditions on the sample variances. The conditional probability of a Type I error, given that sample variances are nearly equal, is also elevated or depressed, sometimes to an even greater extent than the unconditional probability. For various combinations of sample sizes and variance heterogeneity, similar results characterize the Welch t test. These findings imply that researchers cannot protect the significance level and power of the t test by deciding whether to use a pooled-variance or separate-variances version based solely on inspection of sample data.
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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 | 0.095 | 0.481 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".