Your table
Paste per-study estimates. The panel pools each factor and refuses when one combined number would mislead.
Study table
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Parsed preview
| Factor | Study | RR | 95% CI | n |
|---|
Column format
factor + HR/RR with lower & upper CI columns is the usual paper format. Also: log_rr + se. Map columns above if your headers differ.
Panel preview
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Panel verdict
Paste a study table — preview appears on the right as columns map.
Panel discussion
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| Question | Dataset | Verdict | Note |
|---|
About AskBench
A statistical panel checks the evidence before the model speaks — and refuses when pooling is not safe. Numbers from the toolkit, never the model.
Most tools give you an answer. AskBench tells you whether the statistics support one. The toolkit computes; the Skeptic refuses when checks fail.
Reproducible offline: ASKBENCH_STUB_LLM=1 python3 web/server.py (no API key).
Measured over 200 seeds · 1.58% false-positive rate after Benjamini-Hochberg FDR.
Data
AskBench is deliberately explicit about provenance.
- Real published dataset
- BCG trials (Colditz 1994) · 13 studies · benchmark tab
- Synthetic datasets (for measurement)
- Perturb-seq screen · VTE meta-analysis · planted traps
- Reproduce
python3 eval.py·python3 real_data.py- MCP server
- Call the Skeptic from inside Claude Code · askbench_mcp.py
How we measure this
A single good answer is easy to cherry-pick. So both tracks are re-run over 200 random seeds and scored against planted ground truth: every trap the Skeptic must catch, every real finding it must let through, and the rate at which pure-noise candidates slip past. The figure below is generated from those runs, not typed by hand.
Reproducible from a clean clone: python eval.py prints these
exact numbers, deterministically, with no model and no credits spent.