Day 85: Analyzing error: where the pipeline is honest vs where it lies
Reading the error like a detective
Now interpret the error table. Which dimensions are reliable, which are shaky, and *why*? Cross-reference with the provenance (Day 75) and confidence: are the high-error measurements also low-confidence (good — the system knew) or high-confidence (bad — the system was confidently wrong)? This analysis turns raw numbers into understanding, and it's what you'll actually talk about in an interview.
- Dimension patterns: widths (shoulder, hip) often measure better than lengths (arm) that depend on more keypoints; note your actual pattern.
- Condition patterns: how much does off-angle or occlusion degrade each dimension?
- Confidence honesty: the critical check — where confidence was high but error was large is where your system *lies*, and that's the priority fix.
- Source attribution: use provenance to identify which model contributes most to the worst errors.
Confidently wrong is the only unacceptable failure
Uncertain-and-wrong is fine (the system flagged it). Confident-and-wrong is dangerous — a user trusts '94%' and gets a bad fit. Finding and fixing the confident-wrong cases (usually by making some failure mode lower its confidence) is the highest-value output of this analysis. It's the difference between an honest system and a plausible one.
Key terms
- Error analysis
- Interpreting where and why a system errs, cross-referencing error with confidence, conditions, and provenance.
- Confidently wrong
- A high-confidence prediction that is actually incorrect — the most dangerous failure, since users trust it.
Your analysis finds a measurement that is frequently wrong but always reported with high confidence. Why is this the priority fix?
