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Comparing candidates

Putting candidates side by side, what makes a comparison fair, and where scores are not comparable.

In this article

  • Comparing within a stage
  • Where scores are not comparable
  • What to look at beyond the number

Comparing within a stage

Candidates who sat the same stage of the same flow are directly comparable: the same skills, the same weights, the same difficulty and the same evaluation settings. The comparison view puts their per-skill scores side by side.

This is the strongest thing the platform offers, and it is stronger than comparing two human-led interviews, because two human interviewers never ask quite the same question.

Where scores are not comparable

  • Across positions. Different skills and different weights. A 78 on one role and a 74 on another are not a ranking.
  • Across leniency settings. If you changed evaluation leniency mid-round, candidates before and after are on different scales.
  • Across stage types. An MCQ score and a video interview score measure different things.

The comparison view will not put non-comparable candidates on one axis, which sometimes means it shows you less than you wanted. That is the intended behaviour.

What to look at beyond the number

Open the per-skill breakdown. Two candidates on the same overall score are frequently strong in different places, and which of them you want depends on what the team already has — a question the score cannot answer.

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  • Reading an interview report
  • Making a hiring decision
  • How AI candidate scoring works
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