Investigating the robustness of the nonparametric Levene test with more than two groups
Bibliographic record
Abstract
"Testing the equality of variances during hypothesis testing is an important preliminary step before using statistical tests such as the t-test or ANOVA. It has been demonstrated that many tests for equality of variances are sensitive to non-normal distributions. Using computer simulation, the present simulation study investigates the Type I error rate and statistical power of the nonparametric and median versions of the Levene test for equality of variances when there are three, four or five groups used in the analysis. For each of the three, four and five group conditions there are several levels of sample size, variance ratio, group sample size imbalance, and degree of skew in the population distribution included in the simulation. Results show that the nonparametric Levene test shows good statistical properties when samples come from heavily skewed population distributions, when overall sample size was small, and when groups were unbalanced. The findings also allow for a relative comparison of the median-based Levene test of equality of variances under a variety of conditions. Practical implications for the testing for equality of variances are discussed."
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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 | 0.187 | 0.540 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".