Controlling the false discovery rate and increasing statistical power in ecological studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ecologists routinely use Bonferroni-based methods to control the alpha inflation associated with multiple hypothesis testing, despite the aggravating loss of power incurred. Some critics call for abandonment of this approach of controlling the familywise error rate (FWER), contending that too many unwary researchers have adopted it in the name of scientific rigour even though it often does more harm than good. We do not recommend rejecting multiplicity correction altogether. Instead, we recommend using an alternative approach. In particular, we advocate the Benjamini–Hochberg and related methods for controlling the false discovery rate (FDR). Unlike the FWER approach, which safeguards against falsely rejecting even a single null hypothesis, the FDR approach controls the rate at which null hypotheses are falsely rejected (i.e., false discoveries are made). The FDR approach represents a compromise between outright refusal to control for multiplicity, which maximizes alpha inflation, and strict adherence to FWER control, which minimizes power. We review the multiplicity problem, illustrate the advantage of the FDR approach, and promote this approach for widespread adoption in ecology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it