Round 113

Which data-cleaning strategy?

A team is preparing a data analysis pipeline for a large, messy observational dataset collected from multiple sources over several years. The goal is to produce reliable aggregate insights and a model-ready table for downstream analysis within two weeks. The dataset contains duplicate records, inconsistent categorical labels, outliers that may be true values or errors, and a nontrivial amount of missing data. The team must balance statistical validity, reproducibility, implementation speed, and transparency to stakeholders. They also want to avoid overfitting decisions to the quirks of this single dataset, since the pipeline may be reused later. The main question is how aggressive the cleaning and filtering should be: preserve more records with minimal transformation, standardize heavily and remove questionable rows, apply conservative imputation and robust methods, or create separate versions for exploratory and production use. Reasonable experts may disagree because each choice trades off bias, variance, interpretability, and turnaround time.

Dashboard

Status

DECIDED

Winner

A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting.

Majority

A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting.

Duration

5478 ms

Topic

Options

A Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting.

2.12 of 3 PPV winner

D Use a conservative, lightly cleaned pipeline that standardizes obvious formatting issues, keeps most records, and documents all unresolved anomalies for later sensitivity checks.

0.88 of 3

B Apply an aggressive cleaning policy that deduplicates strictly, removes records with substantial inconsistencies, and excludes suspicious outliers to maximize apparent data quality.

0.00 of 3

C Invest most of the time upfront in building a formal data-validation and correction framework, even if it reduces time available for analysis, to standardize future reuse.

0.00 of 3

E Retain nearly all records but use robust statistical methods and targeted imputations so the analysis can tolerate noise without discarding potentially informative cases.

0.00 of 3

Checks

Baselines

PPV no gold answer

Majority no gold answer

Comparison PPV matched majority

k samples8

Agents3

Parsed samples 24 / 24

Quality split agent disagreement

PPV

Top Influences

Claude Sonnet 5 · delegate 1.236

GPT 5.4 mini · delegate 1.106

Gemini 3 Flash Preview · delegate 1.106

Samples

Answer Distribution

Claude Sonnet 5 A: 8

GPT 5.4 mini A: 7 D: 1

Gemini 3 Flash Preview D: 7 E: 1

PPV

Influences

Label	Role	Score
Claude Sonnet 5	delegate	1.236
GPT 5.4 mini	delegate	1.106
Gemini 3 Flash Preview	delegate	1.106

Voting matrix

Constructed Delegation Matrix

To / From	Claude Sonnet 5	GPT 5.4 mini	Gemini 3 Flash Preview
Claude Sonnet 5	0.000	0.106	0.106
GPT 5.4 mini	0.000	0.000	0.106
Gemini 3 Flash Preview	0.000	0.106	0.000
A	1.000	0.789	0.000
B	0.000	0.000	0.000
C	0.000	0.000	0.000
D	0.000	0.000	0.789
E	0.000	0.000	0.000

Embeddings

Sample Embeddings

24 samples

Claude Sonnet 5 GPT 5.4 mini Gemini 3 Flash Preview

Samples

Agent Responses

Claude Sonnet 5 openai_compatible · anthropic/claude-sonnet-5

#0 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

Maintaining two parallel datasets balances the need for fast exploratory work with the need for reproducible, defensible production outputs, allowing the team to preserve records for exploration while applying stricter standards where they matter most for mode...

#1 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

The dual-track approach best balances competing needs: it enables flexible, fast exploratory work while maintaining a documented, reproducible modeling dataset for production. This separation avoids overfitting cleaning decisions to a single strategy, provides...

#2 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

Maintaining two parallel datasets balances the need for fast, transparent exploratory work with a stricter, reproducible modeling-ready table, allowing the team to document anomalies and test sensitivity without prematurely committing to aggressive filtering o...

#3 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

Maintaining two parallel datasets balances exploratory flexibility with rigorous, reproducible modeling outputs, letting analysts explore anomalies freely while keeping a clean, defensible dataset for reporting. This approach also avoids overfitting cleaning d...

#4 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

Maintaining two parallel datasets balances the need for fast, transparent exploratory work with the need for a defensible, reproducible modeling table—rather than forcing a single aggressive-vs-conservative tradeoff. This approach lets the team investigate out...

#5 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

This approach balances reproducibility and transparency by keeping an exploratory version for quick insight generation while maintaining a stricter, well-documented version for production/modeling, addressing the two-week timeline without conflating explorator...

#6 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

The dual-track approach best balances exploratory flexibility with reproducible production output, meeting the two-week deadline while remaining transparent to stakeholders. It avoids overfitting cleaning decisions to this one dataset (unlike aggressive dedupl...

#7 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

The two-week timeline and the need for both rapid exploratory work and transparent, reproducible production output favor a strategy that separates concerns rather than committing to a single aggressiveness level. Maintaining parallel datasets lets the team exp...

GPT 5.4 mini openai_compatible · openai/gpt-5.4-mini

#0 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

A reasonable strategy is to keep both a minimally processed dataset for exploration and a stricter, reproducible version for final analysis. This balances transparency, speed, and statistical rigor, while avoiding irreversible choices that could bias results o...

#1 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

A large messy dataset with a need for both exploratory work and a reliable final table is best served by keeping two versions: one minimally processed for understanding the data and one stricter for reproducible reporting and modeling. This balances transparen...

#2 D · Use a conservative, lightly cleaned pipeline that standardizes obvious formatting issues, keeps most records, and documents all unresolved anomalies for later sensitivity checks. 1536 dims

The scenario emphasizes balancing reliability, transparency, and speed while avoiding overfitting cleaning choices to one dataset. A conservative lightly cleaned pipeline keeps most data, handles obvious issues, and documents unresolved problems for sensitivit...

#3 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

The team needs a strategy that balances analysis quality, reproducibility, and speed while avoiding overcommitting to one cleaning choice. Since they also want both exploratory flexibility and a reliable model-ready table, the best fit is to keep a minimally p...

#4 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

The scenario emphasizes balancing exploratory flexibility, reproducibility, and a model-ready output, while avoiding overcommitting to one cleaning choice for future reuse. Maintaining two versions lets the team preserve information for exploration and apply s...

#5 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

The situation calls for balancing exploratory flexibility with a stricter, reproducible dataset for downstream modeling, especially since the pipeline may be reused later and stakeholders need transparency. Maintaining two versions lets the team preserve infor...

#6 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

The scenario emphasizes balancing reliability, reproducibility, speed, and transparency while avoiding a one-size-fits-all cleaning choice. Creating two parallel datasets best satisfies those competing needs: a lightly processed version for exploration and a s...

#7 A · Maintain two parallel datasets: one minimally processed version for exploratory analysis and one stricter, modeling-focused version for final outputs and reproducible reporting. 1536 dims

The dataset is messy, but the team needs both exploratory flexibility and a reproducible, model-ready product within a short timeline. Maintaining two versions best balances transparency, statistical rigor, and practicality: the minimally processed dataset sup...

Gemini 3 Flash Preview openai_compatible · google/gemini-3-flash-preview

#0 D · Use a conservative, lightly cleaned pipeline that standardizes obvious formatting issues, keeps most records, and documents all unresolved anomalies for later sensitivity checks. 1536 dims

The team must deliver insights within a tight two-week window while ensuring the pipeline remains reusable and transparent to stakeholders. Option D prioritizes speed and reproducibility by focusing on essential formatting while deferring complex filtering dec...

#1 D · Use a conservative, lightly cleaned pipeline that standardizes obvious formatting issues, keeps most records, and documents all unresolved anomalies for later sensitivity checks. 1536 dims

A conservative approach balances the two-week deadline with the need for transparency and reproducibility by avoiding premature data deletion. Standardizing formats while documenting anomalies allows the team to deliver insights quickly without introducing the...

#2 D · Use a conservative, lightly cleaned pipeline that standardizes obvious formatting issues, keeps most records, and documents all unresolved anomalies for later sensitivity checks. 1536 dims

The best approach balances the two-week deadline with the need for transparency, reproducibility, and the prevention of overfitting. Option (D) prioritizes speed and statistical integrity by correcting objective errors while preserving the original signal, avo...

#3 E · Retain nearly all records but use robust statistical methods and targeted imputations so the analysis can tolerate noise without discarding potentially informative cases. 1536 dims

This decision requires balancing a short two-week deadline with the need for a reusable, transparent, and statistically valid pipeline. Option (E) is the most effective approach because it preserves the dataset's information density while using robust methods ...

#4 D · Use a conservative, lightly cleaned pipeline that standardizes obvious formatting issues, keeps most records, and documents all unresolved anomalies for later sensitivity checks. 1536 dims

The best strategy balances speed, transparency, and statistical validity by prioritizing broad data retention while documenting uncertainty. Given the two-week timeframe and the goal of avoiding overfitting to dataset quirks, a conservative approach prevents t...

#5 D · Use a conservative, lightly cleaned pipeline that standardizes obvious formatting issues, keeps most records, and documents all unresolved anomalies for later sensitivity checks. 1536 dims

The team faces a constraint of two weeks to deliver both aggregate insights and a model-ready table from a large, messy dataset while ensuring reproducibility and future reuse. Option (D) strikes the optimal balance by prioritizing speed and transparency, allo...

#6 D · Use a conservative, lightly cleaned pipeline that standardizes obvious formatting issues, keeps most records, and documents all unresolved anomalies for later sensitivity checks. 1536 dims

This strategy balances the need for a two-week turnaround with the requirements for statistical validity and reproducibility. By standardizing only obvious issues and documenting anomalies, the team ensures transparency for stakeholders and provides a foundati...

#7 D · Use a conservative, lightly cleaned pipeline that standardizes obvious formatting issues, keeps most records, and documents all unresolved anomalies for later sensitivity checks. 1536 dims

This decision requires balancing a short two-week deadline with the need for a reusable, transparent, and statistically sound pipeline. Option (D) is the most effective choice because it prioritizes speed and transparency by fixing obvious errors while preserv...

Timings

Stages

Sampling4857 ms

Embedding610 ms

Signals11 ms

PPV0 ms

Errors

Agent and Round Errors

No errors recorded.