8 tips to avoid bias in selection

selection bias

Bias in selection is something you want to avoid. Or the other way around: how do you set up as fair a selection process as possible? Fairness in the selection of new colleagues means preventing bias. Bias results from systematic errors in judgment in the selection process. These errors can lead to

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Evidence-based Selection Methods.

This fact sheet provides an overview of the most commonly used (psychological) selection methods, both classical and modern. The figures are based on meta-analyses and dominant scientific literature.

Method Predictive validity (r) Typical reliability
Cognitive ability (GMA test) .51 High (.85-.95)
Work test .54 High
(inter-rater ≥.70)
Structured interview .51 Medium-high (.60-.75)
Unstructured interview .18-.38 Low-medium (.40-.55)
Integrity test .41 High (α ≥.80)
Conscientiousness (Big Five) .31 Medium-high (α ~.75-.85)
Job knowledge test .48 High (≥.80)
Years of service .18 Not applicable
Video/asynchronous interview (incl. AI) .30-.40 Good at structuring; algorithmically variable
Machine learning / algorithmic models .20-.50 Depends on dataset; generalizability limited
Serious games / game-based work samples .35-.50 High on objective metrics
Social media screening .00-.20 Low and variable

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