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 H0 of earthquake occurrences not depending on the day of the week. He found that his testing result rejects H0, 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 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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.041 | 0.034 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".