Controlling the false discovery rate and increasing statistical power in ecological studies
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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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 | 0.473 | 0.749 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".