confidence-scoring
Compute and interpret MAD-based confidence scores for experiment results. Use when logging experiment results after 3+ data points to determine if improvements are real or within noise.
What this skill does
# Confidence Scoring Determines whether an observed improvement is real or within measurement noise using Median Absolute Deviation (MAD). ## When to Compute - After **3+ experiment runs** in the current segment (including baseline). - Skip if fewer than 3 runs with positive metric values — report `confidence: null`. ## Algorithm Given all metric values in the current segment (positive values only): 1. **Sorted median**: Sort values, take middle element (or average of two middle elements for even count). 2. **MAD**: For each value, compute `|value - median|`. Take the sorted median of those absolute deviations. 3. **Baseline**: The metric value of the first experiment in the current segment. 4. **Best kept**: The best `keep`-status metric value (respecting optimization direction). 5. **Delta**: `|best_kept - baseline|` 6. **Confidence**: `delta / MAD` ### Edge Cases - If MAD = 0 (all values identical): return `null` — no measurable noise to compare against. - If no `keep` results exist yet: return `null`. - If best kept equals baseline: return `null` — no improvement to score. ## Interpreting the Score The confidence score is a multiple of the session's noise floor: | Score | Meaning | Action | |-------|---------|--------| | ≥ 2.0× | Improvement likely real | Safe to trust | | 1.0×–2.0× | Marginal — could be noise | Consider re-running to confirm | | < 1.0× | Within noise floor | Treat as no improvement | ## How to Apply When logging an experiment result to `autoresearch.jsonl`: 1. Collect all positive metric values from the current segment. 2. If count < 3, set `"confidence": null` in the JSONL record. 3. Otherwise, compute MAD and confidence as above. 4. Record the numeric confidence value in the JSONL entry. 5. When deciding `keep` vs `discard`: the confidence score is **advisory**. It never auto-discards. But flag improvements below 1.0× in your ASI notes as "within noise — may not be real." ## Example ``` Runs: [15200, 15400, 14800, 15100, 14600] Median: 15100 Deviations: [100, 300, 300, 0, 500] → sorted: [0, 100, 300, 300, 500] MAD: 300 Baseline: 15200 (first run) Best kept: 14600 Delta: |14600 - 15200| = 600 Confidence: 600 / 300 = 2.0× ← improvement is real ```
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