Four principles for improved statistical ecology
Bibliographic record
Abstract
Abstract Increasing attention has been drawn to the misuse of statistical methods over recent years, with particular concern about the prevalence of practices such as poor experimental design, cherry picking and inadequate reporting. These failures are largely unintentional and no more common in ecology than in other scientific disciplines, with many of them easily remedied given the right guidance. Originating from a discussion at the 2020 International Statistical Ecology Conference, we show how ecologists can build their research following four guiding principles for impactful statistical research practices: (1) define a focussed research question, then plan sampling and analysis to answer it; (2) develop a model that accounts for the distribution and dependence of your data; (3) emphasise effect sizes to replace statistical significance with ecological relevance; and (4) report your methods and findings in sufficient detail so that your research is valid and reproducible. These principles provide a framework for experimental design and reporting that guards against unsound practices. Starting with a well‐defined research question allows researchers to create an efficient study to answer it, and guards against poor research practices that lead to poor estimation of the direction, magnitude, and uncertainty of ecological relationships, and to poor replicability. Correct and appropriate statistical models give sound conclusions. Good reporting practices and a focus on ecological relevance make results impactful and replicable. Illustrated with two examples—an experiment to study the impact of disturbance on upland wetlands, and an observational study on blue tit colouring—this paper explains the rationale for the selection and use of effective statistical practices and provides practical guidance for ecologists seeking to improve their use of statistical methods.
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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 | 0.598 | 0.567 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.006 | 0.088 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.012 | 0.042 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".