Lies or Misuse?: Comment on “Lies, Damned Lies, and Statistics (in Geology)”
Bibliographic record
Abstract
To demonstrate a concern in geological interpretation after statistical hypothesis testing, writing that “geological hypotheses are never ‘true’—they will always be rejected if lots of data are available,” P. Vermeesch ( Eos, 90 (47), 443, doi:10.1029/2009EO470004, 2009) considers a null hypothesis H 0 of earthquake occurrences not depending on the day of the week. He found that his testing result rejects H 0 , and he argues that the hypothesis testing does not reveal any geological significance. We argue that his conclusion basically demonstrates a Type I statistical error, where the null hypothesis is rejected despite being true. Because the use of hypothesis testing crucially relies on three criteria—the correct null hypothesis, a plausible probability distribution, and an appropriate testing statistic—one will easily obtain an incorrect interpretation of statistical significance if one of these criteria is not met. Vermeesch's argument does not exhaustively address whether the last two criteria are met and is insufficient to claim that statistically the hypothesis should be rejected.
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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 | 0.000 | 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 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".