The Most Dangerous Myth in Recruiting: Hiring is an Art

hiring-is-broken-ideal-candidateWith 4 million job openings coinciding with 10.5 million unemployed Americans, both employers and employees are understandably frustrated at a seemingly broken hiring system. In Part I of this Why is Hiring Broken? series, I examine the most common myth in recruiting: “Hiring is an art.”

In a previous blog post, I highlighted the main reasons you can’t trust your intuition when it comes to hiring. However, people are often resistant to using a data-based approach when it comes to hiring—even though it’s been proven with decades of research—to lead to more accurate hiring decisions.

Why Do Recruiters Reject Research?

Based on this article by Professor Highhouse, let’s deconstruct the reasons.

One of the main barriers to data-based decision making is the mistaken belief that hiring is more of an art than a science. The underlying assumption here is that people are too complex to be summarized by a test score and instead, are best judged by another human being. This line of thinking is what fuels the never-ending quest to find the right interview questions, and the right hiring manager with the right instincts, in order to accurately identify the right employee for the job.

Why Hiring Is Not An Art

These assumptions fall apart because human beings are indeed too complex, but paradoxically, we’re too complex to be accurately measured by another human being. In order to do so, you need to collect various sources of information and then integrate this information using sophisticated decision-making rules to come up with a holistic prediction of complicated human behavior – in this case, someone else’s future job performance. In essence, you’re asking someone to conduct a multiple regression analysis in their head.

Okay, but given enough experience we’d be able to do it, right?

Sadly, no. These types of mental calculations are simply beyond our intellectual capacities to do accurately (Ruscio, 2003). We might be adept at collecting the information, but we’re just not very good at combining this information using our judgment in a systematic and accurate way, even when we’re an expert (Kuncel et al., 2013). This is a huge part of why hiring is so difficult and why so many mistakes are made.

A 2013 meta-analysis by Kuncel and his colleagues found that compared to using expert human judgment, an algorithm increases the accuracy of selecting productive employees by more than 50%. In other words, science wins over art in a landslide.

The inherent problem with calling anything “an art” is that people become resistant to adding objectivity and data to the process. An eye-opening experiment by Lievens and his colleagues (2005) showed this to be true: They found that hiring managers were biased toward subjective (vs. objective) hiring methods, regardless of which candidate traits were actually measured.

Their experiment revealed that hiring managers placed more importance on personality than intelligence when they were told personality was assessed with an interview and intelligence was assessed with a psychometric test. The opposite was true when they were told personality was assessed using a psychometric test and intelligence was assessed using an interview.

So why is the idea of using your intuition so popular if it’s been proven to be unreliable?

According to Highhouse, intuitive approaches make the errors in hiring ambiguous and hidden. Data-based approaches, on the other hand, make the errors explicit and visible. This means that compared to data-based decision making, intuitive decision making feels more correct to us because the error involved is intangible.

But as Professor Einhorn argued, it’s when we accept that error is part of a process, that we can start to reduce it. It’s only when you accept the strengths of a data-based hiring process can you start reducing the error in hiring and start fixing a broken system.

In the absence of data, sure, you go with your instincts. But why are you ignoring the decades of data that’s telling us there’s a better way to hire?

Stay tuned for Part II of this “Why is Hiring Broken?” series where I tackle the myth of expertise.

Bonus: Read Wharton Professor Adam Grant’s compelling case for evidenced-based management here.

This is Part I of a five-part series on Why is Hiring Broken?

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Ji-A Min

Ji-A Min

Head Data Scientist at Ideal
Ji-A Min is the Head Data Scientist at Ideal. With a Master’s in Industrial-Organizational Psychology, Ji-A promotes best practices in data-based recruitment. She writes about research and trends in talent acquisition, recruitment tech, and people analytics.
Ji-A Min