Nothing, neither human nor machine, can predict anything 100% of the time. Life is full of unknowns, and if it wasn’t, everything would be pretty boring!
If we were able to predict the right outcomes every time, then the hiring process would certainly be a whole lot easier. You’d simply look through your pool of candidates and identify the best person for the job based on the hiring signals in front of you (resumes, interviews, reference checks, pre-employment tests, etc.). But sometimes, despite all best practices, the best data-driven science, and your better intuition, your predictions can be wrong. When a new hire defies expectations despite all evidence to the contrary, we call that person an outlier.
When you’ve built an objective, predictive, and efficient hiring process that you’re proud of, it can be pretty frustrating to encounter an outlier because it reminds us that our hiring process will never be perfectly predictive. And that’s OK. The first step in dealing with outliers is acknowledging that they’re going to happen.
When it comes to pre-employment tests, outliers are bound to happen a small percentage of the time. For example, an applicant could bomb an assessment but turn out to be incredibly successful in their role. Conversely, an applicant could ace all the tests and still turn out to be a bad hire. These things do happen, they just don’t happen that often. That’s why they’re called outliers.
However, outliers don’t just appear in pre-employment testing – they pop up in just about every possible hiring factor you could use. Let’s take the resume, for instance. An applicant could have a stand-out resume, with exactly the right skills and experience you’d like to see in your ideal employee, but they could still fail to succeed on the job. Likewise, an applicant could have a poor resume with almost no relevant experience and turn out to be a rock star in that position. The same goes for interviews. A sales applicant could be charming and charismatic over the phone and in-person, and still not be able to close a sale.
This is the nature of outliers. Despite various signals in the hiring process, they defy expectations, for better or worse.
So how can you reduce the chance of making a hiring mistake when it comes to outliers? The key is to acknowledge that outliers WILL happen every now and then, and to use that as a reminder to not rely on any one factor too intensely. This is especially true if a candidate’s application is very strong in almost every regard except for one. Let’s say a candidate has a great resume, gave an impressive interview, and has glowing references, but they bombed a pre-employment test. You definitely shouldn’t ignore a score on a pre-employment test – after all, pre-employment tests, and especially cognitive aptitude tests, are significantly more predictive of future job performance than resumes or interviews. But if you’re really impressed by the candidate, ask them to re-take the pre-employment test on-site to see if perhaps they weren’t paying enough attention the first round. If their score improves dramatically, you’ll have less to worry about when hiring them. But if their score stays the same (or gets worse), then you’re stuck in the same dilemma. Ultimately it’s up to you as a hiring professional to weigh each hiring factor to decide whether to hire someone based on the collection of signals you receive throughout the hiring process.
The biggest takeaway when it comes to outliers is to acknowledge that they will happen on rare occasion, and you can reduce their impact by taking a holistic view of your candidates. Weigh all the talent signals you receive to come up with an evaluation of each candidate’s potential. It helps to weigh more predictive factors more heavily than others (for example, weigh pre-employment tests more heavily than resumes), but ultimately it’s the hiring team’s responsibility to come to a conclusion about a candidate’s potential to succeed on the job.