Round 16
Choosing an Experimental Design Strategy
A research team studying how a new soil additive affects crop yield has limited field plots, one growing season, and a fixed budget for measurements. They must commit to an experimental design before planting. The goal is to draw a reliable causal conclusion about the additive's effect while managing constraints: limited replication capacity, unknown sources of field variability (soil composition, drainage, sunlight exposure), and pressure to deliver actionable results quickly. Each design approach below offers a different balance between statistical rigor, practical feasibility, and speed of interpretation. The team must pick one overall strategy to commit to before the season starts.
Status
DECIDED Humans: 0Machine consensus
F · Use a blocked design that groups plots by known soil and drainage similarities, improving control of confounding variables at the cost of fewer usable replicates per condition.Final
F · Use a blocked design that groups plots by known soil and drainage similarities, improving control of confounding variables at the cost of fewer usable replicates per condition.Human vote
No human ballotsTopic
Options
Voting is available while the round is open or extended.
Close reveal
Machine → Final
No human ballots were cast; final consensus follows the machine result.
Checks
Baselines
PPV
Top Influences
Samples
Answer Distribution
PPV
Influences
| Label | Role | Score |
|---|---|---|
| Claude Sonnet 5 (alpha 1.000) | delegate | 1.503 |
| GPT 5.6 Luna (alpha 1.000) | delegate | 1.497 |
| Grok 4.5 (alpha 0.000) | delegate | 1.000 |
Voting matrix
Constructed Delegation Matrix
| To / From | GPT 5.6 Luna (alpha 1.000) | Claude Sonnet 5 (alpha 1.000) | Grok 4.5 (alpha 0.000) |
|---|---|---|---|
| GPT 5.6 Luna (alpha 1.000) | 0.000 | 0.000 | 0.497 |
| Claude Sonnet 5 (alpha 1.000) | 0.000 | 0.000 | 0.503 |
| Grok 4.5 (alpha 0.000) | 0.000 | 0.000 | 0.000 |
| A | 0.000 | 0.000 | 0.000 |
| B | 0.000 | 0.000 | 0.000 |
| C | 0.000 | 0.000 | 0.000 |
| D | 0.000 | 0.000 | 0.000 |
| E | 0.000 | 0.000 | 0.000 |
| F | 1.000 | 1.000 | 0.000 |
Embeddings
Sample Embeddings
Samples
Agent Responses
A blocked design is best because grouping plots by soil and drainage similarities reduces confounding from known field variability. It preserves randomization within blocks while providing more reliable causal estimates than comparing large fields or ignoring ...
A blocked design controls for known differences in soil and drainage, reducing confounding from field variability. Randomizing treatment assignments within blocks preserves causal interpretability while using limited plots efficiently. This is more reliable th...
A blocked design controls for important field variability such as soil composition and drainage while still allowing treatment comparisons within similar groups. This improves causal reliability more effectively than using only large-scale comparisons or unstr...
A blocked design controls for important field variability such as soil composition and drainage while still allowing treatment comparisons within similar plots. This improves causal reliability more effectively than relying only on many unstructured replicates...
A blocked design controls for known field variability such as soil composition and drainage, improving the validity of the causal comparison despite limited plots. It offers a practical balance between statistical rigor and feasibility, whereas the other desig...
A blocked design controls for known field variability such as soil composition and drainage, reducing confounding while preserving causal comparisons. Randomizing treatment assignments within blocks improves reliability despite limited replication and avoids t...
A blocked design controls for important field variability such as soil composition and drainage before comparing treated and untreated plots. This improves the reliability of the causal estimate despite reducing some replication capacity, making it preferable ...
A blocked design controls for important field variability such as soil composition and drainage, improving the reliability of causal comparisons. Although blocking may reduce the number of independent replicates, it is preferable to ignoring localized differen...
Blocking directly addresses the described unknown sources of field variability (soil, drainage, sunlight) by grouping similar plots, which reduces confounding while remaining feasible within limited replication and a single season. This balances statistical ri...
Blocking directly addresses the described sources of variability (soil composition, drainage, sunlight), which the researchers explicitly identified as unknown but likely confounders. Given limited plots and a single season, controlling for these known gradien...
Blocking directly targets the described unknown field variability (soil, drainage, sunlight) by grouping similar plots, which improves internal validity for a causal claim while remaining feasible under limited replication—unlike factorial (dilutes power), ada...
F addresses the described unknown field variability directly by grouping similar plots, which controls confounding factors better than pure randomization or large single-field comparisons. Given limited replication capacity and the need for a reliable causal c...
Blocking directly addresses the described unknown sources of field variability (soil composition, drainage, sunlight) by grouping similar plots, which improves internal validity and confounding control—critical when replication capacity is limited. Although th...
F addresses the described unknown field variability (soil composition, drainage, sunlight) directly by grouping similar plots into blocks, controlling confounding while still allowing valid treatment comparisons—an efficient tradeoff given limited plots and bu...
Blocking directly targets the described unknown field variability (soil, drainage, sunlight) by grouping similar plots, which controls confounding better than pure randomization or scale-focused designs, even though it reduces replicates. Given the fixed budge...
Blocking directly targets the described unknown field variability (soil, drainage, sunlight) by grouping similar plots, which improves internal validity and causal inference despite limited replicates—matching the study's stated constraints better than options...
The problem is about choosing an experimental design strategy for studying anew soot;</figidusllents global additive's препарат effect onプロ crop diesms DeğJesusMiento(TM}"), The team faces tight constraints on plots, season length, and budget, plus unknown fie...
The problem is about choosing an experimental design strategy for studying a soil additive's effect on crop yield with limited plots, one growing season, and fixed budget. The team needs a reliable causal conclusion about the soil additive's effect despite lim...
Timings
Stages
Errors
Agent and Round Errors
Grok 4.5: :timeout