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

Can you select for integrity?

Can you select by integrity

Integrity damage can be caused by anyone in any organization. But there is good news: solutions exist to reduce integrity damage. That’s what this blog is about.

Global talent assessment: US anti-discrimination legislation shoots itself in the foot

US anti-discrimination legislation

Anti-discrimination legislation seems good and logical Thanks to anti-discrimination laws in the US and Australia, asking about an applicant’s country of origin is now off-limits for corporate recruiters. Gender and religion are also taboo. This is to prevent discriminatory selection based on origin, sex, and religion. That there is discrimination based on that data is […]

Talent assessment: when are Young Graduates too young?

Use of emotional intelligence tests

We recently analyzed the assessment data of young graduates from our corporate clients at the meta level. The psychological profile of candidates, who advance to the final stage in selection, shows the following picture: The intellectual abilities of young graduates bring pace to the tent! Relative to the academic average, this group has much higher […]

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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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