Abstract:
This study examines the implicit beliefs that keep organizations from adopting tools to help them make hiring decisions—aids like tests, personality assessments, and other performance predictors.
Specifically, the author identifies two beliefs he argues hinder the adoption of assessment tools:
- People believe it’s theoretically possible to achieve near-perfect precision in predicting performance on the job.
- People believe the prediction of human behavior can be improved through experience.
According to the data, the truth is actually the opposite: performance prediction is an inherently probabilistic exercise subject to great uncertainty (perfection is never possible), and even experienced experts are poor predictors of future performance.
Key Points:
- Predicting someone’s future performance is always an exercise in probability, not certainty.
- Incorporating human judgment into the selection process makes it less accurate, not more accurate.
- Interview-based judgments account for less than 10% of the variance in job performance between hires.
Takeaway:
Hiring selection tools will never be perfect, but the data shows they are both more accurate and fairer than subjective methods of selection like the traditional interview.
