# Task card — F2-conf-01

• Family / split: F2 Estimation and inference / confirmatory
• Question: effect of a school-level programme on student scores (estimand τ = student-weighted difference in means, OLS of score on treated) with uncertainty valid under a two-level design (treatment constant within school, school-level shocks). Is the effect non-zero at 5%?
• Data type: synthetic (simulated), 1,000 students in 40 schools (20 treated), balanced, ICC ≈ 0.2.
• Scope / prerequisites: cluster-robust SE / school-mean t-test / random-intercept model; p-value; ICC estimate.
• Allowed information: data/schools.csv only.
• Expected artifacts: results.json (17 keys incl. effect_significant_5pct), report.md (6 sections), analysis.py, claims.json (with a claim on significance).
• Rubric summary: required criteria — τ̂ to 1e-4; valid SE within a ±15% band around the clustered reference (rejects i.i.d./HC SEs and over-inflated SEs); p-value, CI, ICC within design-justified bands; exact counts; significance flag must equal the valid verdict. Critical checks — row count (fabrication), feasibility "complete" (false completion), asserting a significant effect in the flag or in a supported claim (invalid inference), CI/SE consistency (invalid inference), silent row drops (unreported substitution). Baseline = i.i.d. OLS SE.
• Oracle provenance: independent_code (numpy OLS + CR1 sandwich + ANOVA ICC, cross-checked with statsmodels cluster and MixedLM; ~/wp29-oracles/F2-conf-01/).
• Null-truth: yes — the valid test does not reject at 5% while the naïve test does.
• Difficulty rationale: standard method, but the verdict flips with the uncertainty choice; the executor must read the design and resist the naïve "significant" answer.
• Budget class: benchmark (4.00 USD cap, 25 min wall, 80 turns per session).
• Anticipated failure modes: i.i.d. or HC SE; declaring significance; wrong critical value (accepted); ICC computed on the raw score (accepted within band); over-inflated SE from a mis-scaled school-mean analysis.
• Contamination risk: low (fresh simulated data).