Enhancing understanding of experimental designs: treatment levels and choice of analytics to improve statistical performance for ecological experiments
Bibliographic record
Abstract
Experimental design is a fundamental component of research in ecology and other disciplines. It is critical to understand the consequences of statistical inference, including power and effect size, when making decisions about designing experiments. However, issues such as file drawer effect, funding logistics, and reproducibility are a major concern that often are not considered when starting one’s scientific journey; a poor understanding of these problems may lead to overly conservative estimates or claims that can not be replicated. Here we argue that researchers can dramatically improve inferences from experiments by focusing on two key issues. First, properly manipulating treatment dispersion, which refers to the variation among levels of a quantitative factor, can improve inference without the need for increasing replicates and sample sizes. Secondly, choosing analytics judiciously, such as selecting between ANOVA and replicated regression for experimental data, can improve inference by contrasting inferential outcomes on the same data. We use language, simple fictional examples, and simulations to show that effect size and power increase with treatment dispersion. We also conducted a small meta-analysis on real data to assess whether the literature confirms in published data that treatment dispersion affects inference. We found that there is no association between treatment dispersion and effect size in published literature, suggesting that some form of bias may be prevalent in published literature. Overall, we found that by focusing on treatment dispersion and analytics, researchers can improve their ability to make sound inferences from their data without the need for increased sample sizes.
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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 | 0.261 | 0.621 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".