Skip to main content
Exterview’s scores are only as good as the configuration and inputs behind them. Because scoring is reproducible and calibrated per organization, clean data and consistent usage are what keep results trustworthy over time.

​
Purpose

Give teams a short, practical routine for keeping evaluations accurate, so calibration compounds correctly instead of drifting. See Calibration.

​
Practices

1

Keep roles and rubrics current

Retire stale roles and keep each role’s rubric aligned to what the job actually needs. See How to Set Scoring Weights.
2

Keep candidate data clean

Review incoming candidate records for accuracy and fix obviously bad data before evaluating. See View and Manage Candidates.
3

Use consistent pipelines

Apply the same pipeline and stages for comparable roles, so scores stay comparable across teams. See How to Design Your Hiring Pipeline.
4

Record decisions in the platform

Record score overrides and decisions, with their reasons, in the platform so calibration has real results to learn from. Upcoming: a post-hire outcome loop that builds on them.
5

Review outcomes and recalibrate (Upcoming)

Post-hire outcomes will confirm whether scoring still reflects success, and feed recalibration through change control when it does not. See Outcome Intelligence.

​
Why it matters

Reproducible scoring means the same inputs always produce the same score. If configuration is messy or usage is inconsistent, that guarantee still holds, but it faithfully reproduces bad inputs. Clean data is what makes reproducibility valuable.

​
FAQs

No. Scoring stays reproducible and auditable. The risk is that consistent scoring reflects inconsistent inputs, which good data hygiene prevents.
Calibration compounds from real outcomes. Clean, consistent data is what lets it sharpen rather than drift. See Calibration.