Improved Experimental Design and Analysis for Long‐Term Experiments
Bibliographic record
Abstract
This paper addresses inadequacies in the way most long‐term experiments (LTEs) are conducted and analyzed. The standard design under which LTEs are usually conducted involves a fixed start, establishing all plots in the study in the same year. This design is shown to be inadequate for the purpose of testing and estimating the time × treatment (TRT) interaction, which is generally the primary interest in a LTE. This inadequacy occurs because the repeated measures taken on every plot are all influenced simultaneously by the same random environmental conditions, the effects of which are confounded with the fixed effects of interest. No statistical analysis can completely separate the fixed effects from the random nuisance effects, although added assumptions about the shape of trends across time or covariates to describe the random effects can sometimes be helpful. An alternative experimental design, the staggered‐start design, has been used to alleviate this confounding by establishing plots from different blocks in successive years, but proper analysis of this design has not been presented. A correct analysis of the staggered‐start design is determined and presented. The analysis is applied to hypothetical data from a staggered‐start design whose true means are known, and it is shown to do a much better job of estimating these means than any methods applied to data from the standard design. A staggered start should be considered instead of a fixed start for all future LTEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".