The sample size of the typical ecological correlation coefficient is small and slowly declining
Bibliographic record
Abstract
Larger sample sizes are desirable because they minimize sampling error. However, they are not the only desideratum, and it is unknown if sample sizes in ecology trade off with other desiderata. Here I describe the typical sample size of ecological studies reporting correlation coefficients, describe how sample sizes have changed over time, and develop a hypothesis to explain these changes. Using a database of over 16 000 correlation coefficients reported in 232 meta‐analyses covering a wide range of ecological topics, I find that the median sample size of these 16 000+ correlation coefficients is just 30 observations. This implies that the majority of ecological correlation coefficients likely have low statistical power against the null hypothesis of zero correlation. The typical sample size of an ecological correlation coefficient has decreased slowly since WW II. Decreasing sample sizes may reflect changing research practices. Recent ecology papers tend to report more correlation coefficients, with smaller sample sizes, than they did decades ago. Ecologists today may be choosing to measure more correlations among more variables than ecologists decades ago, even at the cost of smaller sample sizes per correlation. Further research is needed to identify other factors that might drive declining sample sizes of ecological correlation coefficients, and to determine if ecological sample sizes are declining more broadly. It is unclear if declining sample sizes represent a systemic problem for ecological research, given that large sample size is only one of many desiderata in scientific research, and that large sample sizes may trade off with other desiderata. But the trend towards smaller sample sizes should be recognized so that its implications can be discussed.
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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 | 0.174 | 0.494 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".