Why New Hires Fail: The Myth of Expertise in Hiring
In Part I of this Why is Hiring Broken? series, I examined how the myth of “hiring as an art” makes subjective hiring decisions so appealing. In this Part II, I discuss the other main reason why new hires fail: The myth of expertise.
Based on Professor Highhouse’s article, let’s dispel this myth in more detail.
The myth of expertise is the belief that a person can become skilled at making intuitive judgments about human behavior – in this case, a person’s future work success. This expertise is believed to be rooted in innate ability, experience, or a combination of the two. I know what you’re saying: Of course expertise exists. Doesn’t it?
The myth of expertise breaks down with the second part of the equation: The ability to accurately predict human behavior.
We already discussed last week how our brains just aren’t good at accurately combining different sources of information. A groundbreaking 2013 meta-analysis by Kuncel and his colleagues examined over 2200 employees and tested the accuracy of using human judgment vs. an algorithmic approach for predicting workplace outcomes such as job performance. A classic battle between man and machine.
But it wasn’t exactly a fair fight – for the machines: The hiring managers they examined were experts in the jobs and organizations in question and in many cases, they had access to more information about the job applicants than was included in the algorithm.
What were the results?
For job performance, the average correlation was .44 for algorithms and .28 for human judgment. This means that, compared to using expert human judgment, an algorithm increases the accuracy of selecting productive employees by more than 50%.
And it’s not a problem that’s limited to hiring. Across multiple occupations including physicians, judges, parole board members, marketers, financial planners, and auditors, the data reveals experience does not improve predictions of future behavior (Camerer & Johnson, 1991; Dawes, Faust, & Meehl, 1989; Grove et al., 2000; Sherden, 1998).
The evidence is clear: Combining information statistically rather than intuitively leads to significantly better decisions.
Want real world proof? Google almost completely dismisses the importance of expertise. According to Laszlo Bock, Google’s senior VP of People Operations, “The expert will go: ‘I’ve seen this 100 times before; here’s what you do.’” Most of the time the nonexpert will come up with the same answer.”
So why does this myth persist?
In general, decisions based on expertise are more socially acceptable than those based on test scores or formulas (Hastie & Dawes, 2009). In the hiring domain, this means both job candidates and managers may prefer a subjective, intuitive approach to hiring even when they’re well aware of its flaws and limitations.
It makes sense then that hiring managers are resistant to using data-based practices such as psychometric assessments because of the fear that these methods undermine their expertise. But in fact, a data-based approach should always complement your strengths. You still need smart people to use their judgment and expertise to carefully assess the quality of the information being collected, to contextualize and make sense of the statistical analyses, and to decide how the results should be best turned into action. This is the critical thinking that algorithms can’t replace.
Employers, ask yourself: Why do you keep using the same hiring practices and then expecting different results?
Stop wasting time and money on things that the human brain is just not good enough at doing. It’s not always easy, but the first step to adopting a data-based mentality is being able to swallow your pride and admit that the evidence is telling us that there’s a better way to hire.
Stay tuned for Part III of this Why is Hiring Broken? series where I expose the limitations of work experience.
Bonus: Read Andrew McAfee’s HBR article “The Future of Decision Making: Less Intuition, More Evidence.”
This is Part II of a five-part series on Why is Hiring Broken?
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